Bits&Chips 1 | 28 February 2020 | Machine learning

28 FEBRUARY 2020 | 1 MAY 2020

Solaytec hedges its bets with SALD spinoff

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ASML feels the squeeze from US-China standoff

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pinion EDITORIAL Paul van Gerven is an editor at Bits&Chips.

Throwing money

T

he other day, Kees van der Lede, former CEO of Akzonobel and figurehead of Dutch business and industry association VNO, urged the right-of-center political establishment to be courageous. Governments backing off and leaving things to businesses and markets, he contends, just doesn’t cut it anymore.

Hard-working people, stooped in job insecurity, barely reaping the benefits of economic growth, businesses failing to reinvest profits, monopolies cornering markets and many more wrongs – something needs to be done. Just as the left ditched dogma, in the 80s and 90s, to help usher in an era of prosperity, now it’s the turn of the liberal victors to move to the political center, argues Van der Lede.

Van der Lede’s analysis reminded me of some reading I’ve been doing recently. Economist Mariana Mazzucato, too, has been talking up the state, but in a specific policy area: innovation. Her core argument is that primarily the state drives progress, not pioneers pushing through the thicket of government regulation and taxation as is commonly held today.

Take the Iphone, says Mazzucato.

Everything that made this phone revolutionary – features like GPS, the touchscreen and Siri – has a long history of government investment, she revealed. Only when technologies had matured and most of the risk had worn off, Apple swooped in and put them together in an – admittedly – very smart product. So why does Steve Jobs get all the credit? Surely, the government deserves some, if not most.

It rarely gets any these days. In fact, governments are much more likely to be vilified. Good things are often considered to happen despite,

rather than thanks to, governments.

This holds particularly true in Silicon Valley, which ironically owes its success to massive public spending, according to a study by historian Margaret O’Mara. Once Silicon Valley was able to carry its own weight and then some, it turned sour on governments. Only to scurry back to Washington when some foreign threat presented itself, like Japan in the

Risk is socialized and reward is privatized eighties (and, perhaps, China today).

It’s all very reminiscent of banks: risk is socialized and reward is privatized.

Nonetheless, it’s obvious that the public investments that led to the Iphone and Silicon Valley have paid off handsomely in the long term.

Though, they would have done so even more if big tech wasn’t so averse to paying taxes. Silicon Valley is, therefore, not a triumph of the free market but of public-private collaboration. What got it where it is today wasn’t a government-led effort, nor was it the pioneering companies that didn’t need or care about government support. It was a bit of both.

This insight neatly fits in with Van der Lede’s plea to reconsider and re-appreciate the role of governments. It’s also an excellent source of inspiration for revamping Dutch innovation policy, which since the introduction of the Topsectoren also primarily has been based on free-market mythology. Encouragingly, though, one can already see some

of the above insight shine through in recent policy changes, such as the emphasis on key technologies.

Even if we might be on the right track already, however, there’s still going to be a huge obstacle: the money. Getting the Dutch government to invest in education and innovation has been like squeezing blood from a stone lately, and that doesn’t bode well: public largesse was a crucial element in the success of building Silicon Valley.

As the Center for Strategic & International Studies argued recently, throwing money at innovation has become vastly underrated, even if that involves waste in the short term.

“We should shift the debate away from whether to throw money at the problem and toward how best to foster the innovation and return on investment that federal money can enable. The crucial element of successful American money-throwing is its decentralized research and innovation model, in which the government provides competition-enhancing regulation, long-term investment and a market to acquire and use the emerging technologies,” wrote the CSIS. With the world in desperate need of green technology and Europe increasingly getting squeezed between the US and China, let’s take that to heart.

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CONTENTS IN THIS ISSUE OF BITS&CHIPS

8

17

News

News

ASML: if we can’t ship it to China, we’ll ship it somewhere else

Solaytec looking to spread its wings with spinoff SALD

ASML is rather indifferent on the issue whether it should be allowed to sell EUV scanners to China.

ALD tool manufacturer Solaytec is starting a sister company to tap new applications for its technology.

11 News 7 8 9 10 11 14 17 52 54

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ASML’s multi-e-beam metrology tool delayed

Noise ASML: if we can’t ship it to China, we’ll ship it somewhere else ASML is too convenient a target for the US EUV profitability moving towards DUV level ASML’s multi-e-beam metrology tool delayed Seecubic doubles down on glasses-free 3D in Eindhoven Solaytec looking to spread its wings with spinoff SALD Eindhoven startup Maxwaves beams 5G to the max Lightyears away from production

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31

Doctors embrace AI: computer calculates best radiation treatment

Opinion 3 13 30 41 45 51

Throwing money – Paul van Gerven The headhunter – Anton van Rossum My algorithm is better than yours – Marco Jacobs Moving beyond standard AI solutions – Peter de With Digitalization: act now, act fast – Robert Howe Are we prepared for disruptive times? – Biba Visnjicki

Background

18 Qutech: building a quantum delta, one (qu)bit at a time 26 Ampleon: riding the 5G wave with a smart factory 46 Coaching in the third wave of Agile

2020

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EVENT CALENDAR

11 MARCH, ’S-HERTOGENBOSCH

BITS&CHIPS

INDUSTRIAL 5G CONFERENCE

8 APRIL, EINDHOVEN

Background

Qutech: building a quantum delta, one (qu)bit at a time

13 MAY, EINDHOVEN

The Qutech initiative is leading the effort to bootstrap a quantum industry in the Dutch delta.

17 JUNE, ’S-HERTOGENBOSCH

45

Digitalization: act now, act fast

Theme Artificial intelligence

28 High tech prepares for AI 31 Doctors embrace AI: computer calculates best radiation treatment 34 Mastering machine learning with tunable capabilities for eliminating overfitting 38 Lack of funding leaves Dutch AI lagging

7 OCTOBER, EINDHOVEN

Interview

28 OCTOBER, EINDHOVEN

42 90 years of sensing and control – and now machine learning BITS&CHIPS

BENELUX R F CONFERENCE

2 DECEMBER, NIJMEGEN

bits-chips.nl/events

TESTEN EN METEN D e s k u n d i g e p a r t n e r s . M e r k l e i d e r s . O n e i n d i g ve e l ke u z e .

nl.rs-online.com

NEWS

NOISE 5G

Top-10 buyers of silicon, in million dollars Rank 2019 1 2 3 4 5 6 7 8 9 10

Rank 2018 Company 2 Apple 1 Samsung 3 Huawei 4 Dell 5 Lenovo 6 BBK Electronics 7 HP 10 Xiaomi 9 HPE 11 Hon Hai Others Total

2019 spending 36,130 33,405 20,804 16,257 16,053 12,654 10,428 7,016 6,215 6,116 253,224 418,302

Ericsson and Nokia actually could use a little help

2019/2018 (%) -12.7 -21.4 -1.8 -15.0 -9.2 -8.8 -9.0 1.4 -14.6 -7.1 -11.7 -11.9 Source: IC Insights

Apple reclaimed first position in the ranking of biggest buyers of semiconductors, says Gartner. Spending over 36 billion dollars last year, Apple bought 8.6 percent of the market.

Samsung, which ranked first previously, is responsible for 8 percent. The switch at the top is mainly due to Apple’s success in wearable products, such as the Apple Watch and Airpod headphones, Gartner points out. Despite the US-China trade war, Huawei’s IC spending didn’t change much, allowing the company to retain third place. Overall, semiconductor spending was down significantly, however. This was due to said trade war, Brexit, a trade conflict between Japan and South Korea and the protests in Hong Kong. PvG

America’s attorney-general William Barr floated a rather peculiar proposal recently: the US should scoop up the European telecom equipment companies Ericsson and Nokia to help them – and the US – fend off Huawei. The White House quickly dismissed it, but Barr’s suggestion highlights an interesting point about the position of the

European duo in the 5G wars: they might indeed need US backing to stay afloat. Huawei already has a pretty commanding lead, enjoying the advantage of a huge domestic market, the largest market share worldwide and access to cheap state-backed financing. US backing taking shape not as an active intervention but as exclusive access to the American market – Huawei has been banned – might just be the thing that could give Ericsson and Nokia a sorely needed edge. If European policymakers, as promised, also start leveling the playing field with China, Ericsson and Nokia might actually become quite competitive. PvG

R&D expenditure in the Netherlands 18

Private sector

Research institutes

Higher education

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French bulldogs help predict property appreciation

Want to buy a nice house in a safe neighborhood but lack the necessary funds? AI is here to help. US startup Lofty AI has developed a system that predicts property appreciation, allowing users to pick a reasonably priced piece of real estate before its price skyrockets. It works for urban areas and whole neighborhoods, too. Sounds good, right? Where it gets interesting is what information is being used to make the predictions. One parameter, for example, is the number of Instagram posts about French bulldogs. If this is on the rise, chances are that wealthy people are moving into the area. Another indicator is the average wait time for upscale ride-sharing services. If that’s decreasing, relatively well-off people are probably flocking to the neighborhood. Tweets about coffee shops, Airbnb prices, postings for high-paying jobs – the possibilities are endless. One question, though. How can we be sure the predictions are fully based on data and not manipulated by one party or another to make a buck? PvG

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x 1,000,000,000 euro

AI

12 10

4.393 0.900

4.304 0.923

4.506 0.912

4.550 0.968

8 6 4

9.515

10.008

2015

2016

10.654

11.230

2 0

2017

2018 Source: Statistics Netherlands

In 2018, total R&D spending in the Netherlands amounted to about 16.7 billion euros – the highest ever recorded. As a percentage of the gross domestic product (GDP), however, it isn’t a record: the 2018 R&D intensity of 2.16 percent was down 0.02 percent compared to 2017. This R&D intensity is slightly above the European average of 2.11 percent. Only one region in the Netherlands features a significantly higher ‘local’ R&D intensity: the Noord-Brabant province, where it stood at 2.99 percent. PvG 1

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NEWS SEMICON

ASML: if we can’t ship it to China, we’ll ship it somewhere else Whether ASML should be allowed to sell EUV scanners to China may be a geopolitical hot-button issue, the company itself is rather indifferent on it. Its scanners will sell out one way or another.

Paul van Gerven

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rguably, 2019 was an outstanding year for ASML. After two decades of development, EUV-made chips finally started powering electronic devices. The first full-fledged EUV scanners were shipped and EUV bookings now constitute half of ASML’s order book (in terms of dollars). The company grew 8 percent, despite the semiconductor equipment industry as a whole registering an overall decline of 10 percent.

And net sales – yet again – set a record.

None of this seemed of particular interest to the journalists who attended the annual results press conference. They made the trip to Veldhoven for one reason and one reason only: the US-Chinese power play over ASML’s EUV technology. With both the US and Chinese ambassador to the Netherlands weighing in, the issue has been dominating the news recently.

CEO Peter Wennink was a good sport about it, though. None of the press material issued in advance mentioned the awkward circumstance the company finds itself in, 8

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and Wennink didn’t say anything about it during his presentation. But once the floor was opened to questions and the elephant in the room was addressed, he presented his arguments on why the issue doesn’t impact his company all that much – despite the involvement of two superpowers.

“EUV is a driver of so many technologies that we can be sure there will be demand for the chips around the world. If for whatever reason we’re not allowed to sell EUV systems to China, another company will jump in to satisfy the demand. We’ll just ship to that company. It doesn’t matter to us where the chips are manufactured,” Wennink explained. In other words, ASML doesn’t expect to lose a single order. “Financially, the impact is zero.”

Geopolitical arena

Wennink stressed that the risk of the Chinese using an EUV system to steal the technology is nil. “We’re the only company in the world capable of making these systems.

What do you think would happen if we catch them trying to copy it?” Wennink countered.

The scanners themselves are well-protected, he assured, with ASML crews surrounding it 24/7 and sensors alerting Veldhoven even if someone removes a panel.

Should the Chinese want to give it a try anyway: good luck to them. Wennink cited the (in high tech well-known) story of a Chinese university that took apart an ASML scanner years ago, copied every single part to assemble a new machine, yet never succeeded in getting it to work.

“We’re a systems integrator in a collaborative knowledge network. Our knowledge is in the minds of people, not in patents.”

So indeed, should the Dutch government OK the export license to China, ASML will be more than happy to ship the scanner. And if it shouldn’t, that’s fine too. In essence, Wennink tried to divert the spotlight away from his company and towards the geopolitical arena. Let governments worry about it, we have work to do.

ANALYSIS SEMICON

ASML is too convenient a target for the US ASML shouldn’t be singled out as the only semiconductor company to be restricted.

If the US wants to cut off China, at the very least, ban American technology too.

Paul van Gerven

intends to leverage to move up in the world.

Shattering the previously widely held assumption that China would never be able to catch up to the West, or if it did, it would be to our advantage too – the government no longer considers China’s ascension on the world stage necessarily a good thing. Just as technology can change our future, so can a more powerful China.

So, indeed, if the US government has been pressuring the Dutch, it might not have had to press all that hard. It’s not likely that the whole strategy was written merely to appease US wishes, either. Though only the US pursues an aggressive course of action, most Western countries have adopted a more stern stance on China in recent years. The US did – even before the stable genius became president.

There’s much to be said for the Netherlands and Europe rethinking their approach to China, but the US’ pursuit of global dominance isn’t our fight. With that in mind, it’s interesting to note that ASML is a very convenient target for the Trump administration. Curtailing the company will have next to no impact domestically, unlike when chip sales to ZTE

were temporarily banned. So what about, say, Applied Materials? Will its sales be restricted as well? That company wants as much a piece of the action in booming China as ASML does. What about Intel?

Surely its chips can be used to power advanced weaponry.

ASML’s technology is of exceptional strategic importance, though. Cutting off the Chinese from EUV will leave them forever stuck at 7-ish nanometer process technology, preserving the country’s dependence on imports for the most advanced semiconductor technology. To cut it off completely, however, one would have to convince TSMC to stop supplying the mainland (the Taiwanese foundry recently shipped the first EUV-made chips to Huawei).

Still, it doesn’t seem fair to single out ASML when loads of US tech companies are allowed to continue doing business there.

So, unless the Dutch government itself feels it would be wise to cut off China from EUV or use it as leverage in dealings with China, it shouldn’t ban the export. The sad reality of it, of course, is that if the US would really flex its muscles on the issue, the Netherlands would most likely cave.

Credit: ASML

F

inally, ASML finds itself in the eye of a geopolitical battle. It was just a matter of time, once the Trump administration started to put the squeeze on China.

The US is hell-bent on stopping, or at the very least slowing down, the Chinese advance on the world stage. Everything was fine as long as China was manufacturing the easy stuff, but now that it starts posing a threat – economically, technologically and therefore ultimately militarily – to the American hegemony, the fight is on.

According to the reports, initially by Nikkei Asian Review and more recently by Reuters, US pressure led the Dutch government to withhold the license required for ASML to ship an EUV scanner to a Chinese customer. This is almost certainly SMIC, China’s most advanced semiconductor company. At this point, the foundry has no need for EUV in manufacturing. But, the same way Intel, TSMC and Samsung had machines to play with years before they even considered moving EUV into production, SMIC obviously will require some time to learn the tricks of the trade.

I wouldn’t be so sure, however, that it was just US pressure that put SMIC’s order on hold. The Dutch government presented its own China strategy right before ASML’s export license wasn’t renewed. Called ‘A new balance’, it marks a new way of weighing national interests. Basically, short-term commercial interests are no longer drowning out all the other ones. Eyes have been opened to China keenly taking advantage of the possibilities that open, Western economies offer, while not returning the favor at home. The Dutch government acknowledges this asymmetry is both an economic and a national security threat.

The document specifically mentions semiconductors and lithography as powering technological revolutions, which China

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NEWS SEMICON

EUV profitability moving towards DUV level Selling EUV scanners is quickly becoming ‘just another business’ for ASML. It will take another year or two before its profitability is up to ASML’s typical level, however.

Paul van Gerven

I

t doesn’t take an accountant to spot a bit of an oddity in ASML’s 2019 results. The company’s sales grew from 10.9 billion euros in 2019 to 11.8 billion in 2020, yet the net income was the same – 2.6 billion. What gives? The main culprit, it turns out, is EUV. The share of the next-gen lithographic technique in ASML’s sales is growing, but EUV is not yet as profitable as the other activities.

ASML sold 18 EUV scanners in 2018 and 26 in 2019. As a share of system sales, revenue from EUV increased from 23 to 31 percent, and ASML now derives almost a quarter of its total revenue from EUV systems (apart from selling systems, ASML

also gains revenue from what it calls “Installed base management”). The technology everybody had to wait so long for has finally blossomed into a ‘regular’ business.

Except for profitability, that is, which is still lagging. EUV gross margin increased from 20 percent in 2018 to 30 percent in 2019 and will continue to grow to an expected 40 percent this year. Only then will it start to approach ASML’s typical overall gross margin of 45-50 percent. And it will eventually match that, assures CFO Roger Dassen.

“You’ll see an increase in EUV gross margin every time we launch a new model,” explains Dassen. “Every time we do, we

create so much more additional value for our customers that they’re willing to pay significantly more. If we can keep a good grip on cost at the same time, our gross margin will increase.”

Last year, ASML introduced the NXE:3400C, which at around 130 million euros a piece costs 30 percent more than its predecessor. The company shipped some units of it already, but the bulk will follow this year, hence the significantly higher gross margin for EUV in 2020 compared to 2019. In 2021, ASML will launch the NXE:3400C’s successor, increasing EUV profitability yet again.

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NEWS SEMICON

ASML’s multi-e-beam metrology tool delayed After a complication in IP sharing, ASML was forced to start over developing a multi-beam e-beam metrology tool. As a result, the launch of the system was delayed by a year.

Paul van Gerven

Real-time feedback

The integration of e-beam into the holistic litho suite still leaves a lot to be desired as far as speed is concerned. The obvious solution: multi-beam tools. This is another justification for the acquisition: ASML’s deep pockets to fund and accelerate the R&D effort to increase the number of beams.

HMI and ASML have been working on their first multi-beam tool for a couple of years now, featuring nine beams in a 3x3 configuration. It’s mainly intended for process development R&D, but it could also be used in production applications whenever optical inspection just doesn’t cut it.

The question is: where are these tools?

The first ones were supposed to ship at the beginning of last year but weren’t. As it turns out, a setback in IP delayed the tool for almost a whole year.

“Initially, we worked with Zeiss on multibeam, but they were sharing relevant IP with another company, which made it impractical to share with us as well. We would

have had to start a separate company to make that work. Instead, we decided to do it ourselves, even though that meant we had to start from scratch. Hence the delay,” ASML CFO Roger Dassen told Bits&Chips.

Fortunately for ASML, early last year it had the opportunity to scoop up some excellent multi-e-beam expertise when Mapper went belly-up. ASML didn’t continue Mapper’s mission in direct-write e-beam lithography but was more than happy to offer its engineers a new job. Without referring to e-beam technology specifically, ASML CEO Peter Wennink recently said that “we’re very happy with our former Mapper people.”

The first 3x3 tools are expected to be shipped in the current quarter. In the future, ASML intends to increase the number of electron beams, aiming to eventually build e-beam tools that can provide real-time feedback during IC manufacturing in a similar way the optical metrology tools in the holistic suite already do.

Credit: ASML

W

hen ASML acquired US-Taiwanese metrology specialist Hermes Microvision (HMI) in 2016, the Veldhoven company had a clear vision for its e-beam inspection tools: integration in the holistic lithography suite. This assortment of computational and metrology techniques in and around ASML’s scanners helps to make sure that chip patterns are printed correctly on the wafer. Over the past few years, the additional control has become a must-have in chip manufacturing at the leading edge.

The addition of e-beam to the exclusively optical inspection methods in ASML’s suite makes sense. Much like lithography is limited by wavelength in what size features it can print, the minimum detectable defect size is capped for optical metrology. At the same time, as chip features get downscaled, the margins of error shrink and increasingly small defects can ruin entire chips.

So helping chipmakers to find and analyze them thus presented ASML with a nice revenue-increasing opportunity. Electrons can help do that because it’s easier to produce low-wavelength electrons than photons.

E-beam is notoriously slow, however. A single electron beam takes ages to trace the entire surface of a wafer. Unsurprisingly, HMI’s tools were originally used for process qualification and calibration.

ASML and HMI figured that, together, they can speed up e-beam inspection significantly: when the information obtained by holistic lithography is used to direct the e-beam to ‘hot spots’ on the wafer, the search area is dramatically narrowed. So there’s a two-way synergy happening here: e-beam inspection expands the holistic litho suite and the holistic litho speeds up e-beam inspection. All the more reason why it made sense for ASML to acquire HMI.

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ORDER NOW

“ASML’s Architects is an impressive book, a curious book and a book for the curious. (…) Clearly a labour of love by Raaijmakers but nonetheless an easy read.”

Peter Clarke, eeNews, February 1, 2019 “Rene Raaijmakers’ book on the history of ASML is a monumental work in its depth and breadth from ASML’s beginning through 1996. (…) No tech company’s history has ever been covered to such a degree.”

Dan Hutcheson, The Chips Insider, February 1, 2019

techwatchbooks.nl/architects

O

pinion

THE HEADHUNTER Anton van Rossum anton.van.rossum@ir-search.nl

Ask the headhunter K.R. asks: Just over a year ago, after a career of more than 10 years doing chip design at semiconductor companies, I switched to a customer-interfacing technical position in the industry. Although the technology hasn’t stopped fascinating me, I find that my qualities aren’t sufficiently utilized when I’m only sitting in front of a screen.

Being in contact with other people is always very inspiring to me, especially with people in high tech. However, such roles are rare in the part of the country where I live. I was therefore very enthusiastic when I got the opportunity at a renowned international company. The position seemed to fit me perfectly.

For a year, I did the job with great pleasure – I learned something new every day. I got along with my boss, although I didn’t see him often because he spent a lot of time abroad. I also had a great working relationship with my colleagues.

A few months ago, my manager was given a new position at the head office and I got a new supervisor. Initially, my rapport with him was also excellent: we spoke only once, but the contact was good. A few weeks later, however, I was told that my contract wouldn’t be renewed because I wasn’t suitable for the position.

This was a real slap in the face for me. In the year that I’d been working for the company, I’d never heard a word of criticism about my performance. Not that it matters much – the decision is irrevocable.

How to proceed? I no longer want a purely technical function; I want my job to be more about people and

technology. Do you know companies where I can find such a position?

The headhunter answers: You were unlucky to get a new manager right before securing a permanent contract. While your previous supervisor was very content with you, the new guy has doubts. He asked you about your achievements in the past year and wasn’t satisfied with the answer. You haven’t really done anything wrong, but I’m guessing you didn’t show enough initiative towards customers. You should have asked him how he assessed your performance and how you could improve it.

When you have a commercial position, it’s advisable to always get feedback from your colleagues and your

cation engineering). All of these roles put greater demands on your social and contact skills and – in case of a commercial position – on your commercial skills as well.

In my view, you have the potential to grow in such a role, but you need guidance. Appropriate training also seems useful. Your previous employer has really missed an opportunity here. Especially since there are so few candidates in your field who meet the profile, it would have been beneficial to everyone if you’d been given a better chance of succeeding.

By failing to get feedback, you planted a seed of doubt in your manager manager. By failing to do this, you planted a seed of doubt in your manager over your abilities to grow in your position. That finally killed you.

Looking for a position with a focus on people and technology in your field, you have the choice between organizational technical functions (such as project management) and commercial technical functions (such as sales and marketing or field appli1 13

NEWS DISPLAYS

Seecubic doubles down on glasses-free 3D in Eindhoven As 4K technology has become the new standard in video resolution, Seecubic’s glasses-free 3D solution offers a new dimension for OEMs. But after several years of maturation and small-volume production, the company is looking to get back to what it does best: development and innovation for the future.

Collin Arocho

I

n late 2010, former Philips employees were developing a glasses-free solution to bring 3D to televisions. In December of that year, seeking investors to help them bring their innovation to the market, the group had a fortuitous meeting with Mathu Rajan, the founder of a newly started media and technology company called Stream TV Networks. As it turned out, Rajan was on the lookout for 3D technology and was interested in their solution. Within weeks,

the group set up a booth at the Consumer Electronics Show in Las Vegas to unveil their technology: Ultra-D – 3D content on any screen, without the need of glasses. Merely months later, the Eindhoven-based Seecubic was founded as an R&D subsidiary of Stream TV and tasked with developing the technology, components and services of Ultra-D. Today, some eight years later, Seecubic and its parent company seem to have hit their stride as a developer and supplier of

display technology. “We’re a technology provider, not a device maker. Our customers are the OEMs. We want to work with them on integrating our core technology to be used in their devices. They know how to make a computer. They know how to make a TV. They know how to make a smartphone. We don’t want to reinvent those wheels,” illustrates Bud Robertson, CEO of Seecubic. “It’s really about facilitating the adoption and integration of our optics, rendering and content format to participate in the overall content ecosystem.”

Global partners

Credit: Seecubic

It appears this business model is poised for success. Stream TV is currently in talks with a number of global brands, the likes of Lenovo and Huawei, that are interested in adopting its technology in their devices. Recently, China’s BOE Technology Group, which this year surpassed LG as the world’s largest flat-panel display manufacturer, announced it would be partnering with the US company to utilize the glasses-free 3D platform developed at the Eindhoven-based R&D facility.

This surge in international interest stems from, at least in part, the advances in screen resolution technology – ie upgrading from 1080p to 4K and soon to 8K. The faster-than-expected rollout and market acceptance of 4K technology have had a major effect on Seecubic’s roadmap. “We already had a few customers interested at 1080p resolution, but it was clear that making a widespread consumer play was going to be difficult with that lower resolution,” recalls Robertson. “With the arrival of 4K a few years ago, everything really changed

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Credit: Seecubic

for us. Suddenly, we started to generate significant customer interest. This rush of customer interest, however, really shifted the business activities of Seecubic. Suddenly, we were less able to spend time innovating and we spent much more time reacting to customer inquiries, proving that our technology could be adapted to fit their specific needs. We were sort of limited to smallscale production.”

New mandate

Now, a few years further down the road, Seecubic and its parent company Stream TV are again adjusting course. With the technology now to the point where the customer inquiries are satisfied, Stream TV is ready to move its technology to a larger-scale production phase. To facilitate this, the company has added a new product team and facility in Silicon Valley. “The past couple of years have been somewhat restrictive. We’ve had to put a lot of focus on maturing our products and making a lot of demos – sample panels for tablets, phones, TVs,” expresses Robertson. “The new team will be tasked with taking what we’ve incubated in the lab here in Eindhoven and productizing that technology into components that can be used by OEMs. What that really means for us at Seecubic is that

we can get back to our roots, back into the innovation cycle.”

Expectedly, the opening of the new facility and the transfer of this technology to Silicon Valley left some of the Eindhoven employees a little skittish, not knowing what to expect. But to Robertson, the future in the Netherlands is clear. “I have a mandate to double the Eindhoven team over the next year or two. We’ve got a lot of work to do and we’ve got enough innovation projects to keep us busy for many lifetimes.

We just need to develop them and get them ready for market,” says Robertson. “Our new mandate is expansion and innovation.

We have some pretty exciting evolutionary work ahead to establish our technology as the de facto standard over time.”

New flavors

Relieved by the spinout of the new group in America, the Seecubic team has wasted no time getting back to R&D. According to Robertson, there’s never been a shortage of projects and ideas, only a lack of time to focus on them. Robertson: “The new message from executive management in the US is to let the product team figure out integration; Seecubic’s focus should be on creating a new research roadmap and to get us working on some of the projects for

which we haven’t had the time. In all, we have more than a dozen new ventures coming up on the agenda for 2020.”

One such project: integrated depth sensing. Currently, Seecubic’s Ultra-D format uses depth information plus video imaging.

The thought is to collect depth information immediately at the camera, rather than processing the information after the fact.

“We might not need real-time conversion.

We want to develop real-time capture,” highlights Robertson. “That’s one project that we’re now exploring. Of course, it will largely depend on factors like whether current depth sensors are good enough to capture the quality of data that we need. Five years ago, they weren’t.”

Another project that has caught the interest of Seecubic is providing a glasses-free 3D experience for the cinema. “That’s a very different kind of project, but it falls well within the scope of our technology and one where we’re going to make a very big push.

In fact, we’re already in contact with a very high-profile 3D filmmaker who wants to release a 3D film without the need for glasses,” reveals Robertson. “Work like this really gives our guys a chance to put on their creative hats and the ability to focus on inventing new flavors instead of cranking out variations of what we’ve been doing to date.”

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NEWS EQUIPMENT ENGINEERING

Solaytec looking to spread its wings with spinoff SALD Almost ousted from the solar market by Chinese tools, atomic layer deposition tool manufacturer Solaytec is optimistic it can tap new applications for its technology. It’s starting sister company SALD to do just that.

Paul van Gerven

S

High throughput

“The solar market, particularly in China, is currently driven by reducing cost, not by improving performance,” comments Verhage. “This could change, but there’s

Unlike the OEM Solaytec, SALD will focus exclusively on the deposition head and leave the equipment engineering to partners.

Credit: Solaytec

olar alone isn’t a viable market anymore for Solaytec. That’s the cold hard truth, explains Frank Verhage of the Eindhoven-based enterprise. Alarmed by slowing demand, then-parent company of Solaytec, Amtech, hired him in October 2018 to investigate whether the Dutch subsidiary could survive. Verhage’s answer: not without looking at new applications.

Solaytec was spun off from research institute TNO a decade ago to commercialize in-house developed spatial atomic layer deposition (SALD) technology for the solar market. ALD, in general, involves sequentially exposing a substrate to different gases, which react with the surface in a self-limiting fashion, thus ‘coating’ it literally atomic layer by atomic layer. The difference between SALD and traditional ALD is that in SALD, the substrate moves past the gases, while in temporal ALD, it’s stationary while a reactor is repetitively filled and emptied. The continuous character should make SALD the superior technique for industrial applications.

In solar, at least, it was for a while. Compared to other deposition techniques, an ALD’ed layer of aluminum oxide imparts additional efficiency to solar cells, which led several high-end manufacturers to buy Solaytec’s SALD tools (as well as those of Levitech, another Dutch company selling SALD equipment). Unfortunately, as the aluminum oxide layer became more and more mainstream in solar, so did much cheaper Chinese batch ALD tools.

no way to predict when that will happen.

Similarly, perovskite solar cells present an excellent opportunity for us, yet there’s no way of knowing when they’ll emerge from development.”

Fortunately, other high-tech industries have expressed interest in SALD: battery manufacturers, the flat-panel display industry and the printed electronics sector, among others. There are ample low-tech applications as well, such as airtight sealing of packaging materials and treating textile fibers. “As a high-throughput, roll-to-roll compatible technique, SALD is very suited for these applications,” says Verhage.

Solaytec has been working with multiple companies and research partners to explore alternative applications for a while now. Verhage can’t go into detail at this point, but he assures that Solaytec will be ready to offer new solutions in the near future. “We’ve made great progress over the past six months. We just need to wait until the patents are in order.”

Sister company

In light of the expanding application scope, the “Solaytec” brand name is no longer satisfactory. It’s too closely associated with the solar industry, notes Verhage. That’s why Solaytec is starting a sister company, called SALD, which focuses on applications other than solar. SALD is not to be confused with Saldtech, another SALD company spun off from TNO, which is focused on OLED displays.

Unlike Solaytec, SALD’s equipment won’t feature its logo. “Solaytec is an OEM: we design the whole system, and though NTS built it for us, we market it ourselves. SALD is about innovation: our core technology is the deposition head and our core business is how to use it in different applications. We won’t be engineering and marketing the complete tools ourselves. Partners such as VDL-ETG will do that. More generally, in the spirit of the Brainport area, we’ll be actively seeking out collaboration to make the most of this great technology.”

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Quantum technology

Qutech: building a quantum delta, one (qu)bit at a time Even though quantum technologies are only just starting to take shape, now is the time to consider the opportunities they present – both to enhance existing business and to generate new opportunities. In the Netherlands, the Qutech initiative is leading the effort to bootstrap a quantum industry in the Dutch delta.

Paul van Gerven

T

he quantum computer, despite all the excitement surrounding it, is still a mysterious thing. Sure, the theory is solid: scientists have a pretty good idea of what’s needed to build one. But those are the contours, the basic principles. Few would dare to predict what the first fullfledged quantum computer would actually look like on the inside, what technology it would be based on. Even today, several candidates are being considered, and probably more options will pop up – or fizzle out – as time passes.

Still, the quantum computer has moved from concept into reality: already some primitive ones have been built. To outsiders, these systems can’t do anything particularly useful, but the point is that quantum calculations are being performed, which means people are getting their hands dirty.

These quantum builders need to think about the whole system: not just the quantum bits (qubits) that perform the calculations but also the electronics and software that are essential to manage and control them. Just like a PC needs a hard drive and an operating system in addition to a processor, a quantum computer needs peripheral electronics. Actually, it needs a lot of it. It takes a couple of pretty sizable ‘classic’ computers to run a quantum one.

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The elements that constitute the modern computer were developed gradually, one at a time, until they all came together. It started decades ago with the invention of the transistor, then came the integrated circuit (computer chip), at some point, someone started working on the software, and so on.

Quantum Delta NL

Qutech may be the front-runner, but many more Dutch organizations are involved in building the Quantum Delta NL. Most universities have some kind of quantum research program, Qusoft from Amsterdam focuses on application software for quantum computers and the quantum Internet, and the collective of Dutch high tech companies called the top sector High Tech Systems and Materials (HTSM) is keen to stay involved. In September 2019, these organizations presented their joint goals and ambitions in the National Agenda Quantum Technologies. Apart from the commercial ecosystem, the agenda also addresses educational and human capital issues as well as informing and engaging the general public.

That’s not how the quantum computer is being built, explains Ronald Hanson, scientific director of the Dutch quantum research center Qutech in Delft. “Already at this early stage, we have many disciplines working together, both scientists and engineers. Making progress in one particular area depends on progress being made in all others as well. It’s not a step-by-step evolution of isolated technologies.”

Qutech is one of the few places in the world where such a concerted effort is taking place. Google and IBM are the most well-known organizations building complete quantum computers, along with some lesser-known companies, while most research groups focus on a single problem or a few at most. But Qutech is neither a company nor a research group. It’s a public-private collaboration trying to leverage its efforts into establishing a quantum industry in the Dutch delta. How has that been working out, so far?

Imperfect

Qutech was founded in 2013 by TU Delft, a university of technology and engineering, and TNO, an applied research institute. TU Delft had world-class quantum research to offer, as well as expertise in electrical engineering, physics and computer sciences

Credit: Marieke de Lorijn

A quantum computer in progress at Qutech. The big vessel (a cryostat) is where the magic happens.

– all very relevant for quantum technologies. TNO knows a thing or two about those things too, having assisted the international high tech industry in innovation for decades. Another role of Qutech, however, is to reach out to companies and try to get them involved with quantum technology.

Two big fish already signed up: Microsoft, which built its own lab next to Qutech’s premises, and chip manufacturer Intel, which entered into a long-term partnership. Of course, these are the kind of

companies that don’t need convincing that quantum tech is a paradigm-shifting opportunity. “Getting firms on board whose core business doesn’t involve computing as much is a lot harder,” admits Rogier Verberk, director Semiconductor Equipment Industry at TNO and the organization’s point man for Qutech. “But we’re getting more and more traction.”

Roughly three types of companies are potential Qutech partners. First, perhaps surprisingly, there are the end users – the

companies that could take advantage of quantum technology. That may sound strange, given the primitive state the technology is still in, but that shouldn’t deter anyone, Hanson assures. “The perfect quantum computer may be years or even decades away, but before we get there, we may be able to solve real-world problems with imperfect quantum computers.”

Hanson refers to what are called noisy intermediate-scale quantum (NISQ) machines. These are modestly sized systems in terms of computing power and furthermore prone to making errors. Even if lacking compared to the real thing, NISQs could still prove useful for a range of problems that are out of reach of regular (super)computers, because methods can be devised to compensate for the errors.

“The problem is that while we know what those problems look like, we don’t know many that would actually be useful to solve. In fact, it has become a research field on its own to find those,” says Hanson.

“Surely, though, many such problems are to be found at companies. At banks perhaps, to sift through data. Or at pharmaceutical companies, looking to design a new drug.

These companies have no idea what quantum computing could do for them, however.” Which means there’s all the more reason for Qutech to actively reach out and get companies involved.

Already Qutech joined forces with a bank, ABN AMRO, though not for quantum computing but for quantum communication, a branch of quantum technology that’s a little farther along than computing. The project involves exchanging extremely secure cryptographic keys between users in a way that’s practically impossible to eavesdrop on. In the future, these keys could be used to secure Internet and mobile banking. Taking this one step further, Qutech is also working with telecom company KPN to make the quantum Internet a reality.

Business opportunities

The second kind of company that fits in well in the nascent Dutch quantum delta is one that’s involved in the quantum business itself. Qutech is the reason Finnish company 1 19

Building a foundation for the Dutch high tech ecosystem Despite competition from China and the US, the Netherlands continues to play a major role in the world of high tech. Patrick Strating of NTS believes it starts with high tech companies that have close ties to top-notch technical universities and continues with ambitious workers that thrive on life-long learning through training.

To establish and preserve their expert knowledge, the workers at NTS often attend technical trainings in optics, mechatronics and systems development. Perhaps somewhat surprising, however, is the benefit the company sees by emphasizing social trainings like soft skills and sales.

nts-group.nl hightechinstitute.nl/consultative-selling

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Credit: Marieke de Lorijn

Quantum technology

Bluefors decided to open an R&D office on the TU Delft campus, where Qutech resides.

Bluefors designs cryogenic systems for quantum computers. “It’s important for us to be able to design new specifications with leading users and to benefit from each other’s knowledge,” commented Bluefors CEO Rob Blaauwgeers when the Delft office was announced in 2018. More companies might want to follow this example.

Qutech itself gave birth to two quantum startups already, both of which are now operational in Delft. The Qblox spinoff focuses on the previously mentioned spe-

Qutech Qutech is one of many research initiatives co-funded by the Top Consortium for Knowledge and Innovation (TKI) High Tech Systems and Materials. Through the Ministry of Economic Affairs and Climate, TKIs provide additional funding whenever industry invests in long-term innovation projects with public research organizations.

Insides of the cryostat.

cialized electronics and software required to run a quantum computer. Hanson: “You can’t operate a quantum computer using a normal PC. At Qutech, we’ve put a lot of work into electronics that satisfies the specific demands for quantum computing.

Our results are so good that it makes sense to start selling.” The other spinoff, Delft Circuits, focuses on dedicated electronics wiring for quantum computing at ultra-low temperatures. A third spinoff is in the making, but Hanson can’t tell anything about that one yet.

The third and final type of ‘Qutech company’ may be considered both a potential user and a technology supplier. Many high tech companies will be able to take advantage of quantum computing once it reaches a certain performance level, enhancing their toolkit for innovation. At the same time, quantum computing will present new business opportunities. As the technology evolves, there’s likely more revenue to claim. The supply chain will increasingly become more sophisticated, just like that 1 21

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Quantum technology

of – say – the PC is very large and complex these days. “We’re basically shooting for something very similar to the semicon ecosystem we already have here in the Netherlands, with companies like ASML, NXP and their supply chains. And we want to be the best in Europe,” explains Verberk.

Wait, aren’t you the best in Europe already?

Verberk, smiling: “We don’t like to be immodest, but yes, there’s something to be said for that. The large number of people that work here, the world-class quality of

the research, the fact that we successfully brought together scientists and engineers under one roof, and that Microsoft and Intel chose to join ... let’s just say many people admire what we’ve done here.”

Still, there are quite a few places in Europe that aspire to become a quantum hub. That’s a lot of competition.

Hanson: “Ultimately, a quantum hub needs critical mass to succeed. Clearly, we can’t have twenty hubs all over Europe. I suspect we’ll see consolidation over the next few years. In fact, I hope the European Union

What makes a quantum computer so great?

In a regular computer, information is coded into a series of 1s and 0s, the bits. These, in turn, correspond to physical states. The 1s and 0s on a hard disk, for example, correspond to the direction of a magnetic field (up or down) on a large number of distinct areas on a surface. A computer essentially performs operations (manipulations) on a series of 1s and 0s according to the instructions given by the user.

A quantum computer, too, uses physical states to represent 1s and 0s, but – crucially – these physical states are small enough to obey the laws of quantum mechanics.

It’s best for anyone without a degree in physics not to try and understand quantum mechanics, so you just need to know that quantum mechanics allows physical systems to be in two states at once. So, in a quantum computer, where a bit is called a qubit, there’s the 1s, the 0s and the both-1-and-0s.

The both-1-and-0s are where the magic comes from: you can perform two operations for the price of one, ie a single operation can be performed on both a 1 and a 0 at once. A normal computer needs two operations for that.

One or two operations – it may not seem like a big deal. But the difference between quantum and classical computing grows exponentially. A 10 qubit quantum computer can already perform 210 = 1024 operations at once, so imagine the power of a quantum computer with hundreds of qubits!

There is a caveat, however: qubits are extremely fragile and frequently ‘flip’ their value. This means extensive error correction is required, which is made possible by using many physical qubits to make a single ‘real’ qubit. Currently, researchers expect the ultimate quantum computer will need millions of physical qubits, whereas the biggest one now only has tens of (still very fragile) qubits.

Another important thing to note is that not all types of calculations benefit from quantum power. A quantum computer will be great at finding out that 268,568 is 569 times 472, for example. A classical computer has to check every reasonable combination of numbers until the solution is found. Not every problem is necessarily of this nature, though.

Still, there are plenty of applications for quantum computers. They could be used for machine learning, in financial modeling and for logistics, for example. Another great application for quantum computers is in chemistry. Molecules are quantum systems and chemical reactions are dictated by quantum mechanics. What could better simulate these systems than an actual quantum computer? Imagine being able to design new drugs or materials without ever setting foot in a lab.

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will encourage this. And I’m confident that Qutech will be one of the ecosystems to make the cut.”

Are companies already lining up to start working with you?

Hanson: “Compared to other countries, such as Germany and the United States, I must say that Dutch companies have been quite cautious. I’d really like to see things speed up. We need companies to get on board and, reversely, I don’t think it’s an exaggeration to state that this technology is too important to miss out on.”

“On the other hand, we have some improvement recently. Before we started Qutech, I invited twenty companies or so, gave them a tour and told them what we were setting up. Afterward, I asked them whether they supported our concept, whether they would like to stay involved and whether they would like to invest in quantum technology. They all liked our idea and wanted to stay involved, but that was it. Nowadays, their interest is much more tangible. Only a few years ago, ABN AMRO would never have invested in quantum technology. And recently, we signed a long-term collaboration agreement with KPN.” Verberk nods: “Not that long ago, quantum technology wasn’t an item at trade conferences, not even at technology conferences. Today, dedicated parallel sessions are popping up all over the place. Slowly but surely the awareness grows. Our meetings with companies used to be generally exploratory in nature. These days, we dig a lot deeper.”

It must be hard to convince companies without having something to show them, though.

Hanson: “We have demonstrator programs!

We’re building a small quantum Internet and we have a quantum computer that outsiders can use for getting to know the technology and its possibilities. Right now, it’s a simulation running on a supercomputer, but we want to start offering real quantum hardware in 2020. Having such goals is an excellent way to drive our own efforts, by the way. Because everything will need to be

Credit: Marieke de Lorijn

on point. Every single part of the computer will need to be up and running and working well with other parts.”

IBM has had a similar program for a long time, on real hardware. Google recently had its quantum computer outperform a supercomputer on a specific problem for the first time in history. It seems like you’re falling behind.

Hanson: “What Google did, is a breakthrough. No doubt about that. Yet, I don’t think we can compare what we are and what Google is trying to do. Google chose a very specific challenge five years ago: to demonstrate quantum supremacy. They succeeded,

which is fantastic. But that result isn’t very relevant to Qutech’s mission.”

“As for IBM, it did very well to start offering quantum computing to the public.

That’s necessary for educational purposes and for community building – two things that are really important for a radical new technology. That’s very much like Qutech’s mission: educating people – TU Delft started a new series of master courses Quantum Technology – and building an ecosystem.”

High Tech Highlights A series of public-private success stories by Bits&Chips

Verberk: “We’re not competing with Google and IBM, and it’s not our mission to outdo Google and IBM. What matters is what we do to bring quantum technology to fruition in the Netherlands.”

There’s a long road ahead of you. What are the biggest obstacles?

Hanson: “Apart from getting companies involved, the biggest bottleneck is talent.

Quantum technology needs a lot of qualified people, but there simply aren’t that many of them around. There’s a reasonably steady supply of young researchers, but experienced talent is hard to come by. That’s definitely going to be our biggest problem in the coming years.”

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INVENTED

BY ALL

A profession in design or technology isn’t typically what most youngsters aim for when they’re in high school. Partly this is because of the lack of realization of what design and tech mean for the world. Actively confronting them with their own ability to design something that means a lot to others increases their motivation for an innovative study or job. This is exactly what the project “Invented by All” does.

L

iving at home with dementia Everyone has heard of it, lots of people suffer from it: dementia, or Alzheimer’s disease. It not only affects the patients but their families as well. For people developing dementia, living on their own often becomes more difficult. Together with October – a health facility near Eindhoven – the Discovery Factory engaged (14-year-old) youngsters in high school, asking them if they could come up with solutions.

This generated lots of innovative ideas helping patients with devices for daily needs or security.

The kids also looked into the social element of bonding with patients.

A whole new game At the Rythovius College in Eersel, one group developed a board game especially for families having a member suffering from memory loss.

The game aims to stimulate conversation and interaction between patients and caretakers.

A group from Sint-Lucas – a school for the creative – further developed the concept.

At the end of their project, they presented a prototype, well tested with the target group.

Their “Talktogether” game is now ready for use, complete with a wooden board, appealing questions and a format for playing a game together. The whole point of the game? All win!

Your own “Invented by All” project The real winners are the kids who developed the game. They found out that what they design and make really matters to people! If you have aspirations in enthusing school children for science and technology as well, or the company you work for has, the “Invented by All” project could be your starting point. The context and societal impact of your company are the inspiration for the pupils. If you need assistance, you can always call on our professionals for help.

The Discovery Factory is there to inspire youngsters for a future in design and technology. Projects are supported by tech companies such as ASML, Brainport Industries, Daf Trucks, Frencken Europe, Hager, NTS Group, Philips, Stam en De Koning and VDL Group, and by Bits&Chips as the media partner.

discoveryfactory.nl

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Industrial automation

Ampleon: riding the 5G wave with a smart factory A leading global partner in RF power products, Ampleon works on developing new 5G technologies, setting up the intelligent factory and professionalizing its supply chain. The Factory Intelligence program is a roadmap to bring factory automation to the next level and to use data to increase performance and predictability levels. Ampleon’s Head of Global IT, Lody Hoekstra, talks about the journey, use cases and challenges of modernizing the company’s factory.

Rolf Naberink

H

eadquartered in Nijmegen, Ampleon is a global player in the area of RF power. Its core business is the production of technology that amplifies communication signals for a wide range of applications, such as mobile broadband (3G, 4G, 5G), radio and television broadcasting. The company was established in 2015 after NXP was required to carve out its RF Power business.

This carve-out meant that the new organization needed to establish a new IT environment within a short period. “An enormous challenge, but it also offered us a chance to improve and prepare for the future,” explains Lody Hoekstra, Head of Global IT at Ampleon. Hence, the company decided to build a scalable digital platform.

Ramping up 5G

The completion of Ampleon’s new digital platform marked the start of a new chapter: a rapid ramp-up. Hoekstra: “5G technology was coming fast. In a short time, we needed to more than double our production capacity. This resulted in several challenges, with two key areas that we needed to focus on.”

“First, the supply chain. As we were scaling up our production, the demand for flexibility grew. Time-to-market needed to be reduced. We also required more insights and more control over the process.”

The second area that Ampleon needed to focus on was the factory itself. “We operate a factory that employs 1,200 people 26

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in the Philippines. And when you need to increase your manufacturing output that much, using new technologies, launching new products at a faster rate than ever before, this creates a tremendous challenge for the organization.”

Developing Factory Intelligence

Ampleon’s management posed the question: how can we modernize our factory?

This produced a list of conditions and goals, such as becoming paperless, decreasing the man-machine ratio and full traceability of

products. Hoekstra: “80 percent of these initiatives are IT driven and we recognized that we needed to prepare for this.”

Ampleon teamed up with the digital consultants of Itility to embark on the development of a new program: Factory Intelligence 2022. The starting point was to define concrete use cases that provide business value for the company and translate those into a roadmap.

“Building a smarter factory involves using data. Lots of data,” says Hoekstra. The building of a data factory was, therefore,

view. Change management is a big part of innovation.

Use case: predictive maintenance

one of the first steps to take. Based on a central data lake, in which all Ampleon’s manufacturing data is collected, governed and made available.”

“You need a solid architecture, a technical configuration and full integration. This process requires not only IT but domain expertise as well, combining the efforts of infrastructure engineers, data engineers, and business analysts.”

When building a data factory, it can be difficult to keep stakeholders involved. This was solved by using a roadmap to build for the future while focusing on small deliverables. Like a single data source or dashboard: things that immediately make people happy.

Use case: real-time OEE monitoring

Gathering data is step one, but the real goal is to use that data to improve daily operations. The challenge is to balance ambitious, long-term improvements with deliverables that immediately provide value. One example of this is real-time monitoring of the overall equipment effectiveness (OEE).

“As a first step, we built a new factory-wide yield report and real-time monitoring of our OEE. Presenting data on machine uptime,

usage and performance in a way we can analyze it and take action.”

Hoekstra continues: “This tool is a great improvement compared to our previous systems. It provides immediate, actionable insights that are easily accessible for our entire organization. We now have a single source of truth for OEE information.”

Use case: traceability

End-to-end traceability is important, both for customers and Ampleon itself. “If a product is returned to us and we suspect it to be flawed, we want to be able to completely backtrack that product through our factory. Which machines did the product go through, in which phase? What values were measured at that time? If the cause of a flaw is confirmed, we can immediately identify which other clients might be impacted.”

Achieving this kind of traceability takes a big effort. First, the manufacturing equipment itself. Ampleon uses industrialized IoT devices and smart machines to extract data. After disclosing this data, it’s vital to know that people need to adjust as well. Operators who are used to look at a single machine need to adopt a holistic

Another important goal in Ampleon’s Factory Intelligence program is predictive maintenance. “We’re working towards a scenario in which we know exactly when each machine was serviced, which components were replaced and what the status is of components that are being used right now. We’ll use this information to predict when maintenance is necessary and optimize maintenance actions. This helps us to minimize a machine’s downtime, which creates a big cost saving. If minimizing downtime in combination with an optimized factory scheduling means we can deliver the same output with 80 percent of the machines previously required, that saves millions of euros.”

The first step in predictive maintenance is to make basic but important information available. For example: how many strokes have been made with a specific tool? This helps to replace the tool before it breaks down. Instead of basing maintenance on the time that has passed, you can use the real usage of a tool to replace it just in time, thus preventing replacements that are unnecessary or too late. The next step is to actually use machine learning to even better predict when a replacement is needed.

Moving forward

Over the past years, Ampleon has successfully taken several major steps in their smart factory journey. As a result, the company is already seeing a significant increase in its efficiency, quality and output. “We would never have succeeded in multiplying our production in only a few years using our old way of working. Digitalizing our factory wasn’t just a luxury, it’s a necessity to prepare for Ampleon’s future.”

Looking at the future, Hoekstra expects to see a lot of developments in the area of artificial intelligence. “There’s a lot of potential if we can enable our software to identify risks and opportunities by itself. We’re now working with data reporting and analysis.

The next step is to automate the data analysis as well and use more complex algorithms to start predicting and prescribing.”

Rolf Naberink works at Itility.

Edited by Nieke Roos

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THEME ARTIFICIAL INTELLIGENCE

HIGH TECH PREPARES FOR AI Artificial intelligence promises mountains of gold. As smart algorithms are spreading like wildfire in the high tech industry, engineers are facing all kinds of obstacles. AI specialist Albert van Breemen tells us what tech companies have to take into account if they want to master the technology.

Albert van Breemen

A

rtificial intelligence is a technology that, due to its versatile applicability, enables radical changes in many industries.

AI has developed rapidly over the last eight years – even faster than experts predicted. This is mainly due to deep learning, an AI technology requiring a huge amount of data to work properly. The first wave of commercial AI applications has emerged at companies that already have lots of data, like Amazon, Facebook, Google and Uber. Currently, we’re seeing a second wave in which AI algorithms are being trained with sensor data. AI is entering the

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engineering world and traditional machine builders are faced with the question of whether and how to invest in artificial intelligence.

Designing advanced systems is getting ever more challenging. Each generation, their complexity grows.

There’s an increasing demand for performance, intelligence and interoperability. Engineers regularly reach the limitations of their current toolbox, in which modeling and analysis from so-called “first principles” are standard. Adding AI gives them technologies to work with larger datasets (big data) and smart AI algorithms. Both elements form the ba-

sis for finding new solutions to the aforementioned challenges.

In practice, however, companies are finding it difficult to embrace artificial intelligence. A good case study could convince management to invest more in AI. Unfortunately, the people who have potential cases within a company have no experience with the technology and fail to recognize the opportunities. This makes it difficult to demonstrate the benefits and secure an innovation budget. In addition to business-driven barriers, we also see many operational obstacles in practice, such as the complexity of the AI software and hardware,

the great diversity of algorithms (when to use which technique), the computing power needed to train models and finding talent.

Barrier 1: the AI technology stack

The technology stack for artificial intelligence is a complex set of hardware and software components. Model training requires an incredible amount of computing power because it uses computationally intensive gradient-descending and backpropagation techniques. This calls for special hardware, such as CPUs, tensor processing units (ie AI-specific computing cores), FPGAs or ASICs. Applying a trained model to an embedded system requires different hardware, designed with energy consumption and system resources in mind.

The AI software stack is built up of a series of modules, most of which are developed independently of each other. Updating one of the modules often causes it to stop working smoothly with the rest. In addition, server-side software libraries, such as Tensorflow, aren’t always compatible with the libraries used on embedded systems.

Barrier 2: algorithm diversity

Deep learning algorithms can be subdivided into a number of categories, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and reinforcement learning. Within each of these, there are a number of more specific algorithms. Which one to choose isn’t only determined by the application, like object classification or object detection in CNNs. It also depends on the software stack and the hardware used, as well as the model size and accuracy and the type of input data,

for example. Optimizing a model is a time-consuming process.

Barrier 3: computing power

The demand for computing power to train modern AI algorithms doubles every three to four months. Training an algorithm like Deepmind’s Alphagozero requires more than a million teraflops of computing power. To put that into perspective: a modern GPU does about fifteen teraflops. According to a conservative estimate, training an AI algorithm for the Starcraft game will soon cost several million euros. Most companies lack the resources for this. Fortunately, many other useful AI algorithms can be trained with less computing power, but the standard IT infrastructure of most companies is often inadequate for this as well.

Barrier 4: talent

Many machine builders have recently experienced the introduction of artificial intelligence within their company. They have to catch up because they often are unaware of the value that data and AI can bring to their products. In addition to the challenge of bridging the gap between engineers and AI experts, they’re also in a fierce competition with larger and well-known AI companies to at-

Albert van Breemen is speaking at the second edition of the Machine Learning Conference, 11 March in ’s-Hertogenbosch. mlcon.nl

tract AI specialists. Universities are currently working hard to train new talent. Eindhoven University of Technology’s Eindhoven Artificial Intelligence Systems Institute (EAISI), for example, is starting a number of new master’s programs this year that close the gap between engineering and AI.

Companies at bat

Current developments in artificial intelligence will have a major impact on products and industries.

Occasionally, you can hear people saying that we’re too late and everything happens in the US and China.

But that’s not true. The success of an AI application in engineering strongly depends on domain knowledge.

And that’s just where Dutch companies excel, for example in mechatronics and human-machine interaction.

Knowledge institutions and organizations such as the High Tech Systems Center and EAISI are working hard to remove the barriers. Collaboration with companies is key to achieve this and to make a new voice heard: AI and engineering takes place in Brainport.

Albert van Breemen manages the AI program at the High Tech Systems Center (HTSC) and the Eindhoven Artificial Intelligence Systems Institute (EAISI) of Eindhoven University of Technology. He founded the AI Engineering Lab and is working together with FME, the Dutch employers’ organization in the technology industry, to make AI more accessible for SMEs. He’s also the owner of the AI consultancy agency VBTI. Together with High Tech Institute, he teaches the masterclass “Introduction to deep learning”.

Translated by Nieke Roos

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pinion

ARTIFICIAL INTELLIGENCE Marco Jacobs is a marketing and strategy consultant.

My algorithm is better than yours

I

n 20 years in developing electronics, I’ve heard it a lot: “Our product is so much better! The algorithms our engineers developed are way beyond what our competition has.”

In the 1990s, I saw it with my own eyes at Philips. Their TVs had the best deinterlacing algorithms in the world, resulting in superior picture quality. In the 2000s, I worked on audio enhancement: turning on sound processing algorithms gave quite a stunning effect. Tiny speakers would suddenly create bass and provide a 3D sound stage. Over the next years, I’ve seen cameras getting a much better picture quality, virtual reality headsets providing a much smoother experience and compression algorithms becoming a lot stronger. The before and after effect is a very strong sales tool.

Now, we’ve entered into an era where it’s not about picture or audio quality anymore. Instead, our electronics need to become smart and adopt AI. Andrew Ng from Stanford put it well: “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” Even though AI can only solve fairly simple tasks, this presents a lot of business opportunities. The market responds and big corporations acquire AI teams for large sums of money.

In AI, I’m seeing the same “my algorithm is better than yours” claims.

Many companies state their AI is better than everyone else’s and are showing the before and after effect. Kudos to these companies, for all having managed to hire the smartest AI algorithm engineers? No, not really.

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With AI, for one, it’s actually fairly easy to build your own algorithm.

Instead of having engineers that skillfully craft and program the algorithm, AI is simply trained. You give the AI engine lots of examples over and over again and it keeps adjusting itself until it doesn’t make any mistakes anymore.

Another problem is that it’s hard to measure ‘better’ in AI land. There’s a famous saying that states there are lies, damn lies and then there are

AI doesn’t seem like a great foundation on which to build a solid company benchmarks. In AI, it’s no different.

In automotive, for instance, you can measure false negatives, where you don’t detect a person in front of the vehicle. But a false positive, where the AI brakes for a pedestrian who’s not there, is almost equally bad.

The size of the data set is something to consider as well. It sounds impressive when an AI algorithm scores perfectly on 10 million kilometers of automotive test data, but since we have one billion vehicles on the road that each drive 10,000 kilometers a year or so, the AI probably still only covers a fraction of the real-world situations that can occur.

Even perfect scores can be meaningless in such situations.

A final complicating factor is that the algorithms are still rapidly changing. There are many competitions where universities and corporate research centers battle it out and continuously introduce new algorithms. The winning neural networks are freely made available on the web, as are the tools to adapt and train them. Download and retrain the model for your target application and you’re done.

Thus, we come to the conclusion that AI algorithms are easy to develop, hard to benchmark and ever-changing. That’s a problem because it doesn’t seem like a great foundation on which to build a solid company. Strong companies typically operate in markets that have high barriers to entry, making it difficult for new entrants to come in and compete, which isn’t the case with AI. My advice: don’t rely solely on AI in isolation, but use it as an enabler for your business and closely integrate it into your products. Simply focusing on “my algorithm is better than yours” won’t give you a sustainable competitive advantage. When your engineers tell you that their algorithms beat the competition, congratulate them, but ask them right away how they’re planning to maintain that advantage.

Credit: Ivo van der Bent

THEME ARTIFICIAL INTELLIGENCE

DOCTORS EMBRACE AI: COMPUTER CALCULATES BEST RADIATION TREATMENT A computer that can calculate an entire series of optimized treatment plans for prostate cancer in 30 seconds. That took some getting used to for medical specialists. Using artificial intelligence, the computer presented better plans and more insight than the doctors thought possible. The first patients are due to be treated accordingly already this year.

Aschwin Tenfelde

S

urprised and amazed, that’s how radiotherapists felt at Amsterdam University Medical Center, AMC location. A computer using artificial intelligence generated an entire range of plans for the radiation of prostate cancer in no time.

Not only that, but these plans were also completely optimized and even specified what the outcome would be of employing a lower or higher dose of radiation.

It usually takes even the most experienced radiotherapist with a lab technician a great deal of time to determine the best radiation treatment for a patient. So how good can a radiation plan be if a computer seems to generate it so effortlessly?

Radiation dilemmas

Nevertheless, the need for improvement was felt strongly by doctors. For them, it’s always a race against the

clock to decide on the right radiation plan for brachytherapy, or internal radiation therapy. This kind of treatment is a weapon doctors often use to fight prostate cancer.

Radiation is administered through ten to twenty catheters that are inserted into the patient, penetrating the tumor. They then deliver a source of radiation, which can briefly stop at certain places. The longer the source stops, the more radiation the tumor receives from that place. As catheters are extremely uncomfortable for patients, doctors give themselves an hour’s time at most to devise the best possible radiation plan.

The difficulty lies in the considerations doctors are constantly having to weigh. Preferably, they would simply attack the tumor with a hefty dose of radiation. On the other hand, you have to minimize the damage to the surrounding healthy tissue. Unfortu-

nately, there’s a limit to the precision of the radiation beam. It weakens as it penetrates tissue, but it essentially will go through everything. As a result, some radiation inevitably ends up in healthy areas, where it does more harm than good.

This presents physicians with dilemmas: how do you plan to deliver radiation accurately through catheters? And how high should the minimum dose be? How much radiation will ‘leak’ to the surrounding tissue, and is that acceptable? The ideal plan is different for every patient.

With sufficient experience and practice, radiotherapists and lab technicians can usually create a plan together within an hour – one single plan, that is. With the current software they use for this, and in light of the time pressure, it’s not feasible to also give extensive thought to alternative radiation plans.

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THEME ARTIFICIAL INTELLIGENCE

Self-learning software

Isn’t there a better way to do this?

That’s the question radiotherapists, clinical physicians and researchers at the AMC put to Peter Bosman in 2014. Bosman, who’s affiliated with the Centrum Wiskunde & Informatica (CWI) in Amsterdam, was studying AI techniques at the time. He was already focusing on the question of how to use artificial intelligence for medical applications, especially new ways of doing medical image analysis.

The fact that doctors posed this question is unusual, according to Bosman, because it doesn’t reflect how things normally transpire in the medical world. “Doctors are used to working within the possibilities provided by the software they use. They know this software like the back of their hand. It’s usually the medical companies that bring innovations to the clinic.”

Bosman accepted the challenges and put together a research team with the AMC and Elekta, which develops radiation equipment and software for hospitals. The team decided to develop software that’s centered on a kind of AI called evolutionary algorithms. These algorithms are suitable for effectively and efficiently finding good solutions to difficult problems, especially when there are multiple conflicting goals at play.

The team focused mainly on a type of evolutionary algorithm that displays ‘intelligent search behavior’: it can learn about the nature of a particular problem and find better solutions more rapidly. Bosman’s team adapted the algorithms so they could be used for brachytherapy in cases of prostate cancer. They did this by allowing them to use knowledge about how the dose of radiation builds up in the administered catheters. This made it possible to achieve far superior results than with other algorithms.

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Pepsi challenge

“Initially the AMC radiotherapists reacted reluctantly to our proposed approach,” recalls Bosman. “Which is completely normal. Imagine, a couple of mathematicians and computer scientists drop by telling that AI can probably do a better job than doctors and their current software, despite their years of experience. We needed time to persuade them.”

The major turning point came two years ago. At that point, the team had made enough progress to do a kind of ‘Pepsi challenge’ with the doctors. “We had the computer create radiation plans for patients who had already been treated in the past at AMC,” explains Bosman. “In other words, the doctors had already made plans for these patients that had been approved.” Subsequently, all plans were anonymized, so the doctors couldn’t check who had developed them. “Then we asked several doctors: which plan would you prefer to use with this patient? In 98 percent of the cases, they chose the plan generated by the computer.”

Bosman’s team also demonstrated that the computer could immediately present an entire range of alternative radiation plans that used lower or higher doses. “The doctors viewed this insight as a unique added value,” says Bosman, “because it gives them time to think about which radiation plan they want to choose, instead of devoting all of their time to developing a single plan. They can now decide how much radiation to deliver to the tumor and how much the healthy tissue can receive.”

From CPU to GPU

The doctors fully embraced the idea and were in favor of using it in the clinic. To reach that point, the team first had to augment the software’s processing speed. At the time of the Pepsi challenge, it still took about an hour to

calculate the range of radiation plans for one patient – and that’s not quick enough for clinical practice. Bosman’s team eventually managed to reduce that time to a mere 30 seconds.

This huge gain in speed was mainly achieved by no longer processing the calculations on the system’s central processor (CPU) but on the graphic processor (GPU). It’s a trick that many researchers apply to get more computing power in simulations, graphics and AI. The gain in speed is derived from the fact that the average GPU has thousands of cores that can carry out calculations simultaneously, whereas a high-end CPU has to make do with eight cores.

You can’t simply ‘switch’, though.

Algorithms made to run on CPUs don’t automatically run on GPUs. That’s because the cores of a GPU are far simpler than those of a CPU, and you often have to use them in blocks.

Bosman: “So you need to look carefully at which calculations really benefit from being run on a GPU. Essentially, they all have to be really simple and you have to want to carry out a large number of them simultaneously. In our case, for example, you want to know how much radiation will be delivered to a huge number of areas.

Also, you want to be able to update that frequently as you search for good radiation plans.”

It’s striking that Bosman’s team gets by with a relatively simple consumer GPU, which costs around 1,200 euros. Although these GPUs lack error checking in the accompanying internal memory on the card, the margin of error turns out to be extremely small. This leads to margins of uncertainty in the generated treatment plans that are completely negligible. That’s also why everyone in AI uses these kinds of cards instead of the more expensive ones that do have error checking, according to Bosman.

Credit: Ivo van der Bent

Good rapport

The collaboration between doctors, researchers and Elekta was excellent.

But that’s certainly not something you can take for granted, says Bosman. “It requires a lot of coordination. Our team’s work is spread across CWI, the AMC and the Elekta office in Veenendaal. To make sure that everyone’s on the same page and familiar with each other’s jargon, all researchers get together twice a week, alternating between CWI and the AMC’s premises. In addition, we go to Elekta every four weeks, and every six weeks, we have an update meeting that everyone attends, including the doctors and clinical physicians.

This allows us to go over the results together and discover what’s important right now. It has enabled us to become a genuine team.”

Bosman strongly advises researchers to continuously seek a good rap-

port, if they want to achieve innovation in a similar way. “I’ve also seen examples in the past of how things can go wrong. Despite making clear agreements and project plans in advance, researchers have the tendency to go their own way sometimes. This can cause lines of research to be cast adrift from the practical use that was the point of it all in the first place.”

In the meantime, the partnership between CWI, the AMC and Elekta is thriving. The hospital is hoping to treat its first patient with the radiation plan created with the new AI software before this summer. “Elekta is also interested in commercializing the results of the project,” Bosman says. “And that could mean worldwide clinical application.”

Follow-up

The big advantage of the AI system is that it’s relatively easy to extend to

other types of cancer. Indeed, a follow-up project was inevitable. With a grant from KWF Kankerbestrijding (Dutch Cancer Society), Bosman and his colleagues are now going to focus on cervical cancer.

The research will be conducted by a consortium that also includes Leiden UMC and almost all Dutch hospitals that treat cervical cancer with brachytherapy. They’ll test and evaluate the new software that emerges from the project. Elekta is on board again as an industrial partner. And so it’s probably only a matter of time before artificial intelligence can be used as a weapon against this form of cancer as well.

Aschwin Tenfelde is the communications manager at Centrum Wiskunde & Informatica.

Edited by Nieke Roos

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THEME ARTIFICIAL INTELLIGENCE

MASTERING MACHINE LEARNING WITH TUNABLE CAPABILITIES FOR ELIMINATING OVERFITTING By integrating scalable software tools with tunable machine learning capabilities, engineers and scientists can efficiently identify the most suitable model that fits the specific industrial data and meets the model objectives, while protecting against overfitting.

Paola Jaramillo Garcia Mohamed Anas

E

ngineers and scientists are building smarter products and services, such as advanced driver assistance systems and predictive maintenance applications, driven by analytics based on industrial data.

Analytics modeling is the ability to describe and predict a system’s behavior from historical data using domain-specific techniques for data preparation, feature engineering and machine learning. Combining these capabilities with automatic code generation, targeting edge-to-cloud, enables reuse while automating actions and decisions.

By leveraging the increased availability of ‘big industrial data’, compute power and scalable software tools, it becomes easier than ever to use machine learning in engineering applications. ML methods ‘learn’ information directly from the industrial data without relying on a predetermined equation as a model and are particularly suitable for today’s complex systems. However, two of the most common challenges faced by engineers and scientists who are modeling with machine learning relate to choosing a suitable ML model to classify their domain-specific data and eliminating data overfitting.

Classification models assign items to a discrete category based on a 34

1

specific set of engineering features extracted from the historical data.

Determining the best model often presents difficulties given the uniqueness of each data set and the desired outcome. Overfitting occurs when the model is too closely aligned with limited training data that may contain noise or errors. An overfitted model is unable to generalize well to data outside the training set, limiting its usefulness in a production system.

Choosing a classification model

Choosing a classification model type can be challenging because each has its own characteristics, which could be a strength or weakness depending on the problem. For starters, you must answer a few questions about the type and purpose of data: what’s the model meant to accomplish? How much data is there and of what type? How much detail is needed? Is storage a limiting factor? Answering these questions can help narrow the choices and select the correct classification model.

You can use cross-validation to test how accurately a model will evaluate data. Afterward, you can select the best-fitting classification model.

There are many types of classification models. A common type is logistic regression. Because of its

simplicity, this model is often used as a baseline. It’s applied to problems that have two possible classes into which data may be categorized.

A logistic regression model returns probabilities for how likely a data point belongs to each class.

Another common type is k-nearest neighbor (kNN). This simple yet effective way of classification categorizes data points based on their distance to other points in a training data set.

It has a short training time, but it can confuse irrelevant attributes for important ones unless weights are applied to the data, especially as the number of data points grows.

A third common classification model type is the decision tree. This model predicts responses visually and it’s relatively easy to follow the decision path taken from root to leaf. It’s especially useful when it’s important to show how the conclusion was reached.

A fourth common type is the support vector machine (SVM). This model uses a hyperplane to separate data into two or more classes. It’s accurate, tends not to overfit and is relatively easy to interpret, but training time can be somewhat long, especially for larger data sets.

A fifth common type is the artificial neural network (ANN). These networks can be configured and

trained to solve a variety of different problems, including classification and time series prediction. However, the trained models are known to be difficult to interpret.

You can simplify the decisionmaking process by using scalable software tools to determine which model best fits a set of features, assess classifier performance, compare and improve model accuracy and, finally, export the best model. These tools also help users explore the data, select features, specify validation schemes and train multiple models.

Eliminating data overfitting

Overfitting occurs when a model fits a data set but doesn’t generalize well to new data. This is typically hard to avoid because it’s often the result of insufficient training data, especially when those responsible for the model didn’t gather the data themselves. The best way to avoid overfitting is by using enough training data to accurately reflect the model’s diversity and complexity.

Data regularization and generalization are two additional methods you can apply to check for overfitting. Regularization is a technique that prevents the model from over-

relying on individual data points.

Regularization algorithms introduce additional information into the model and handle multicollinearity and redundant predictors by making the model more parsimonious and accurate. These algorithms typically work by applying a penalty for complexity, such as adding the model’s coefficients into the minimization or including a roughness penalty.

Generalization divides available data into three subsets. The first set is the training set, the second is the validation set. The error on the validation set is monitored during the training process and the model is fine-tuned until accurate. The third subset is the test set, used on the fully trained classifier after the training and cross-validation phases to test that the model hasn’t overfitted the training and validation data.

There are six cross-validation methods that can help prevent overfitting. K-fold partitions data into k randomly chosen subsets (or folds) of roughly equal size, with one used to validate the model trained with the remaining subsets. This process is repeated k times, as each subset is used exactly once for validation.

Holdout separates data into two

subsets of a specified ratio for training and validation. Leaveout partitions data using the k-fold approach, where k equals the total number of observations in the data. Repeated random subsampling performs Monte Carlo repetitions of randomly separating data and aggregates results over all the runs. Stratify partitions data so both training and test sets have roughly the same class proportions in the response or target. Resubstitution uses the training data for validation without separating it. This method often produces overly optimistic estimates for performance and must be avoided if there’s enough data.

ML veterans and beginners alike run into trouble with classification and overfitting. While the challenges can seem daunting, leveraging the right tools and utilizing the validation methods will help apply machine learning more easily to real-world projects.

Paola Jaramillo Garcia is a data scientist at Mathworks. Mohamed Anas is a regional engineering manager at Mathworks.

Edited by Nieke Roos

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11 MARCH 2020 VERKADEFABRIEK ’S-HERTOGENBOSCH

Silver sponsor

Sponsors

MLCON.NL

#BCML

Powered by

PROGRAM Chair Aad Vredenbregt (Valoli) 09:30

Registration

Keynote 09:45

AI on the edge: interacting swiftly and surely in a dynamic world Nicolas Lehment (NXP)

10:30

Break

11:10

Less is more: do 20 times more machine learning with 95 percent less compute Menno Lindwer (Grai Matter Labs)

11:50

Building custom AI hardware for edge computing Nick Destrycker (Edgise)

12:30

Lunch

13:30

Targeting energy waste in homes using AI and IoT data Stephen Galsworthy (Quby)

14:10

Deep learning in healthcare: cuff-based blood pressure estimation is a thing of the past Vincent Janssen (Verhaert AI Lab)

14:50

Break

15:10

Hybrid machine learning and domain engineering knowledge for real physical machines Guillaume Crevecoeur (Ghent University)

15:50

Quickly finding performance drivers in the high-dimensional data streams of advanced lithography systems Vincent Aarts (ASML)

16:30

Break

16:50

Barriers to adopting AI for engineering Albert van Breemen (TUE)

17:30

Networking dinner and drinks

Subject to change

THEME ARTIFICIAL INTELLIGENCE

LACK OF FUNDING LEAVES DUTCH AI LAGGING Several initiatives to promote AI research in the Netherlands have emerged over the past two years. Bits&Chips asked foremen Max Welling, Frank van Harmelen and Maarten de Rijke to highlight the importance of artificial intelligence for Dutch economy and Dutch society.

Jessica Vermeer

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here are three major players on the worldwide AI stage: China, the US and Europe. America has been leading in investments. China is quickly catching up as a top contender, specializing in machine learning – which is also the main focus of the US. The power of Europe stems from its broad basis.

“Europe has a strong history in artificial intelligence,” says Frank van Harmelen, a professor in the AI department at the Vrije Universiteit Amsterdam (VU). “To this day, most publications on AI originate in Europe.” However, Europe is highly divided. In China, investments are led by a centralistic state, whereas in the US, the investments come from big tech companies like Google and Microsoft. The European Committee decided large investments in AI are necessary for Europe to keep up with the Americans and the Chinese.

While Europe realized the importance of AI investments and started organizing the field, the Dutch political landscape seems to be still lagging. Van Harmelen feels the

Claire

Netherlands has much to improve upon in terms of decisiveness. “Here, we endlessly look for support, bring parties together and organize roundtables in The Hague – it just takes so much time. In the meantime, research hurtles on.”

“AI is just an immensely important part of modern technology and

Frank van Harmelen is a professor in the AI department at the Vrije Universiteit Amsterdam and one of the Dutch intermediaries of Claire.

The Confederation of Laboratories for Artificial Intelligence Research in Europe (Claire) has quite some similarities to Ellis. They’re both research networks and communicate with the European government. Claire consists of 350 research groups in 34 countries and, thus, doesn’t limit itself to EU member states. The Netherlands is strongly represented, with national intermediaries Frank van Harmelen from UvA and Jeroen van den Hoven from Delft University of Technology. Holger Hoos from Leiden University is one of the driving forces at the European level.

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economy,” says Max Welling, a professor in machine learning at the University of Amsterdam (UvA). He compares artificial intelligence to the arrival of computers or the Internet. “All technologies will advance using AI. That also poses challenges for society. We need to keep up with the revolution. Right now, we’re falling behind. Not because we don’t have the knowledge, but by how our society is responding.”

Our surrounding countries – England, France, Norway, Germany and Belgium – have all announced major investments. The Netherlands hasn’t invested at all. “We were one of the first to have AI bachelor’s and master’s programs. Other countries decided to make large investments.

We did not,” states Maarten de Rijke, a professor of AI and information retrieval at UvA. Welling concurs: “We’ve been writing reports, but there haven’t been any real investments yet.”

Talent and capacity

On a European level, Claire and Ellis are the major initiatives for collaboration on AI research (see boxes).

In the Netherlands, the Dutch AI Coalition was introduced to bring all initiatives together and function as a lobby towards The Hague. Welling: “It’s highly important to inform Dutch politics on the necessity of AI investments. Within The Hague, very few people have knowledge in this area. The Dutch AI Coalition will

Credit: Bob Bronshoff

Max Welling is a professor in machine learning at the University of Amsterdam, a co-founder and board member of Ellis and the director of the Amsterdam Ellis unit.

explain to them why AI is so important to the Dutch economy, how society is changing as a result and what we need to do to prevent us from falling behind.”

Welling has a prominent role in Ellis. “Ellis believes machine learning is the driving force behind the AI revolution,” he describes. “There’s an enormous increase in economic activity of AI. According to us, this is because of data-driven AI, also called machine learning.” Many other research topics, like robotics, language processing and image processing, use machine learning as their inherent technology. Ellis’s broad selection of topics stems from its bottom-to-top proposal structure. “We do have a selection committee, but in principle, they’re all grass-roots.”

Within the Netherlands, Ellis collaborates with the Dutch AI Coalition and Claire. Claire originated from an EU request. The European Committee expressed its willingness to invest in AI but stated it couldn’t communicate with hundreds of separate research groups all across the continent. The field needed to organize itself and come up with one organization to talk to. “That’s how

Ellis

Claire came to be,” explains Van Harmelen, who’s one of the initiative’s Dutch intermediaries.

“Generally, the next steps are expected to result from combining reasoning and learning, combining older techniques with technology developed during the last ten years,” Van Harmelen continues. This calls for a broadly oriented network with expertise from different angles in AI. “That’s the goal of Claire.”

Last December, Claire opened its European headquarters in The Hague. “The Netherlands traditionally has a strong position in the research field of AI,” tells Van Harmelen. “Large investments are needed if we want to maintain that position. In that light, The Hague is a logical choice.”

The lack of investments inspired another initiative in the Netherlands, which doesn’t mediate between research and politics: ICAI (see box). Together with colleagues from UvA and VU, De Rijke started it about two years ago because they saw the strategic role AI would play in society and felt something needed to happen. “If the Netherlands wants to remain in strategic control, we need to have the talent and the capacity to understand, design and develop that technology ourselves.

The European Laboratory for Learning and Intelligent Systems (Ellis) focuses on machine-learningdriven AI. Its primary goal is to create a European network of excellence. Its first measure was to set up 11 research programs, all of which focus on a specific topic. Each program was assigned 15 fellows, all leading members of the research community. The second measure was to establish the Ellis units. These are virtual organizations set up across Europe, to which Ellis faculty is appointed. The faculty members and fellows can recruit PhD candidates and postdocs. About 1.5 million euros a year is involved in each unit. In the Netherlands, Amsterdam and Delft have an Ellis unit. In Belgium, there’s one in Leuven. Max Welling is a co-founder and board member of Ellis and the director of the Amsterdam unit.

ICAI The Innovation Center for AI (ICAI) is a network of research labs. Each lab is a five-year collaboration between one or more knowledge institutions and companies. It needs to have at least five PhD candidates, amounting to an investment of about 2 million euros. Right now, ICAI has 11 labs, with about one hundred researchers, in Amsterdam, Delft, Utrecht, Nijmegen and soon in Tilburg, Maastricht and Eindhoven. The target is to have 20 labs by the end of 2020.

To achieve this, we organized several labs under the ICAI umbrella. We don’t have any funding to offer, but we do share experiences, a vision on how collaborative research can be conducted between academia and industry and we connect different parties. Don’t wait, but act. That gives a strong sense of ownership.” Next to being the ICAI director, De Rijke is also in charge of one of the research labs and involved in two others.

Reap and regulate

Especially ICAI demonstrates that research institutes and companies can easily find each other and push forward. The government, however, remains absent. Since 2016, many countries worldwide have presented AI strategies on how to invest. “The Netherlands now finally has presented an AI vision, but, unfortunately, still hasn’t invested at all,” laments De Rijke.

“You would hope to create more speed at the national level,” states Van Harmelen. Welling agrees: “I hope for some movement within the Netherlands. We’ve been founding initiatives, but we really need to invest in the technology and the people that have the knowledge. Not only to reap the economic and societal rewards but also to regulate how we want to shape our society.”

1 39

Robots of tomorrow are being made now

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pinion ARTIFICIAL INTELLIGENCE Peter de With is a professor of video coding and analysis at Eindhoven University of Technology.

Moving beyond standard AI solutions

R

ecently, I attended a urology congress at the Amsterdam Medical Center (AMC), where a series of presentations was given to an audience that predominantly consisted of medical professionals.

Significant parts of the talks were devoted to automated cancer detection and treatment, eg in the prostate or the bladder. It struck me that the proposed solutions showed a great deal of congruence.

This congruence, beyond any doubt, is the result of standardization of convolutional networks and the proven variants of them, like the popular VGG neural network for object detection or the U-Net for segmentation. These networks have been widely tested and evaluated and have shown to give almost optimal or state-of-the-art results for their application areas. Furthermore, the hardware and software vendors offer dedicated packages, which deploy smoothly and execute efficiently on prescribed GPUs or CPUs. This democracy of high-tech applications is a great achievement of artificial intelligence. Only some superficial understanding is sufficient to apply sophisticated deep learning to medical problems. I was pleasantly surprised by a physician’s presentation that showed state-of-the-art results for cancer detection in a urology field. The work was of high quality, although the presenter was neither skilled in processing imaging data nor in the machine learning used for finding the cancer.

At the conference, I also noticed something else: the divide between technical people and medical personnel is not as pronounced as it used to be some years ago. In the recent past, physicians were worried about

the big wave of AI that was rolling in.

Meanwhile, the AI people concerned themselves primarily with the performance of their solutions, while their practicality was of much less concern.

We’re close to the point that the democratizing effect of standard AI solutions has found a sufficient base in the ever-growing group of medical experts embracing this new technology. As university researchers, we’re increasingly confronted with requests to enhance the methodology

AI researchers have to move on to the second wave of AI of a featured solution in the presentation of our work. These trends imply that AI researchers have to move on to the second wave of AI. Indeed, there’s still much to be gained. Most of the established successful solutions in analysis and decision-making are based on combinations of detecting something (like a cancerous polyp) and then classifying it (is it dangerous/malignant?).

U-Net is such an example that combines detection and segmentation into one system. This solution is applicable to images but doesn’t scale to moving video.

The video signal domain is essentially different from high-quality still images because a stream of pictures is used over time, like 30 frames per second, albeit that each individual picture is of lower quality than a photo. This concept is still at an im-

mature stage for AI. For the medical domain, there are multiple applications with video, like endoscopic imaging and interventional imaging with catheters.

Another important emerging area of AI-based applications in the medical domain is “explainable” AI. At present, many users of AI have no idea what the network is learning from all the images and, thus, do not know what causes the network to fail at decision-making. Evidently, this topic is highly relevant for the medical boards that are approving medical equipment. Let the companies show what their systems learn from the data and when and how the network fails in decision-making.

The high-tech industry, too, stands to benefit from a second generation of AI systems. For equipment manufacturers, the robustness and safety of using AI is a crucial point. When a failure occurs – and this will certainly happen – it shouldn’t have dramatic consequences (such as an autonomous car overlooking traffic and making wrong decisions). These kinds of safety aspects are often neglected or not deeply analyzed in system design. Finally, for equipment engineering and the industry in general, cost always plays an important role. Embedded networks on an affordable platform also belong to this second generation of AI systems and applications.

All these aspects present a clear roadmap for researchers and system developers in the Netherlands and Belgium for the upcoming years.

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Credit: Vincent van den Hoogen

INTERVIEW TIM FOREMAN (OMRON)

90 YEARS OF SENSING AND CONTROL – AND NOW MACHINE LEARNING Driven by the needs of society, Omron has spent nearly 90 years developing innovative technologies to enhance people’s daily lives. According to the company’s European R&D manager Tim Foreman, this takes a commitment to keeping employees challenged and motivated by helping them enhance their skillsets with training.

Collin Arocho

P

erhaps you’re not familiar with Omron, but one thing for certain, you’ve benefitted from its technology. From its first innovation of accurate x-ray control timers, to the magnetic strips on credit cards, early ATMs and digital blood pressure monitors used at doctor’s offices – the company has been at it

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for more than eight decades. “Our philosophy has always been, to create value based on the needs of society,” describes Omron’s European R&D manger, Tim Foreman. “Society changes, and we’re continuously adapting to find innovative solutions to newfound issues. That’s what keeps us at the leading edge.”

Employing some 40,000 people around the world, Omron has received numerous recognitions, including a spot on the Derwent Top 100 Global Innovators by Clarivate Analytics and a top ranking on the Down Jones Sustainability Index, which uses several indices to track sustainability efforts by publicly traded companies. “To

make it on these lists is a great honor for us at Omron,” expresses Foreman. “It shows that not only are we among the innovation leaders in our field, but as a company, we keep our focus on the environment and do it in a smart way.”

Tsunagi

Originally known as Tateishi, the Kyoto-based Omron has built its foundation on two key technologies: sensing and control. For instance, remote control devices in automobiles that detect your proximity to your vehicle, before automatically unlocking the doors as you get within a close distance. “It was these electronics components, the switches and relays inside of devices, that really got the business going,” explains Foreman.

More recently, as technology has advanced, the company added a third core focus, referred to as “think,” aka machine learning.

The spotlight, however, isn’t simply on developing individual products, it’s about providing outstanding customer service. Behind the core technologies of Omron is for example the service known as Tsunagi – a Japanese word that translates to “filler”. “Tsunagi means that in your house, if you find a crack in the wall, you fill it in and repair it,” illustrates Foreman. “In the electronics business, it’s common to source parts from different vendors.

Perhaps you choose Omron’s IPC, but another company offers you a highly specialized sensor that you need. The two parts should be compatible, but sometimes the user will get an error message. Rather than place blame or leave the customer to contact others, at Omron, we look to fill in the cracks. We tell our customers, no matter the issue, call us.

We’ve put together some 400 small manuals to make systems work seamlessly and to provide expertise in interoperability – that’s tsunagi.”

Stakeholder management

With a portfolio of more than 200,000 products, Omron’s focus on interoperability and integration is a crucial part of the business. Not ev-

erything can be perfectly integrated, and when you’re dealing with different global offices, that can get tricky.

An example, a safety sensor developed in Italy needs to work seamlessly with a control device developed in the Netherlands. This relies heavily on the communication abilities between the groups. “If the two sides fail to talk, it becomes immediately clear to our customers,” says Foreman. “That’s why we place a real emphasis on communication during the entire development of new products. When things are seamlessly integrated, our customers can clearly see the benefit of what we offer.”

To achieve enhanced communication between units, Omron’s R&D manager turns to trainings and courses. “We have some incredibly

It’s all about stakeholder management – a very expensive and very important term bright employees at Omron, all of them very technically gifted, be it in software, mathematics or electronics.

But while their technical skills may shine, it’s a much smaller percentage that also have strong social skills,” clarifies Foreman. “While especially skilled, our engineers sometimes don’t have the tools or experience to effectively convey their message. In the high tech world, that’s an essential piece to the puzzle.”

“You have to know how to sell your story and motivate others on the team. Furthermore, when you know you’ve got a good idea, you need to know how to approach upper management and convince them. It’s all about stakeholder management – a very expensive and very important term,” continues Foreman. “That’s why we turned to High Tech Institute to help us create Omron’s Talent Academy Training. They speak the right language; they understand the ecosystem and help give our boys and

girls the tools to greatly improve on these skills and others.”

Motivation

This isn’t the only benefit that Foreman sees with training his employees.

“It’s really just a question about how you keep talented workers, especially in the competitive high tech industry.

The answer is simple: you’ve got to keep your people motivated. But how do you do that? Of course, you start by giving them a good salary, but that’s not enough. It’s done by giving them interesting challenges that apply to real-world issues and offering them state-of-the-art tools, equipment and training to tackle these issues,” claims Foreman. “It’s about creating a working environment where they can have fun and enhance their personal knowledge and skillsets. When these criteria are met, it shows in the final product and ultimately, improves its popularity on the market. What better motivator is there?”

It’s precisely these efforts to retain its talented workforce that are perhaps more telling than the total number of workers employed by the electronics company. At Omron, there are some workers that have been with the business for more than 30 years already. “These people have spent thousands of hours with their machines. They can be 10 meters away from them, hear an unusual noise and instantly know what the problem is,” boasts Foreman, himself a 26-year employee of the company.

“But to be perfectly honest, that’s not a modern practice anymore.

Nowadays, employees want to get a variety of experience – to try a little of this and a little of that.”

Omron’s solution: offer its employees broad access to various trainings for individual improvement. At the same time, the company works in-house to develop and apply computer learning models that allow machines to learn from the experienced operators. “The machines can then fill in the gaps and help guide a newer generation of operators,” suggests Forman. “That’s the sort of technologies we’re currently working to develop at Omron.”

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hightechinstitute.nl

Advanced feedforward & learning control

SOFT SKILLS & LEADERSHIP

30 September – 2 October 2020 (3 consecutive days)

Mechatronics system design – part 2

Time management in innovation

5 – 9 October 2020 (5 consecutive days)

Starts 5 March 2020 (1,5 day)

Advanced motion control

Effective communication skills for technology professionals – part 1

26 – 30 October 2020 (5 consecutive days)

16 – 18 March 2020 (3 days + 1 evening)

Metrology & calibration of mechatronic systems

27 March 2020 (1 day)

Actuation and power electronics

30 & 31 March 2020 (2 consecutive days)

Dynamics and modelling

8 & 9 April 2020 (2 consecutive days + 1 evening)

Design principles for precision engineering

How to be successful in the Dutch high tech work culture

27 – 29 October 2020 (3 consecutive days)

Creative thinking – full course

16 – 18 November 2020 (3 consecutive days)

Consultative selling for technology professionals

23 – 25 November 2020 (3 consecutive days)

Benefit from autism in your R&D team

23 – 27 November 2020 (5 consecutive days)

14 April 2020 (1 day)

OPTICS

Effective communication skills for technology professionals – part 2

Modern optics for optical designers – Part 1

Improve the power of your speech

Modern optics for optical designers – Part 2

22 – 24 April 2020 (3 days + 1 evening)

Starts 18 September 2020 (15 weekly morning sessions)

27 May 2020 (1 day)

Leadership skills for architects and other technical leaders Starts 2 June 2020 (2 times 2 days + 2 evening sessions)

ELECTRONICS

Power integrity for product designers

3 – 6 March 2020 (4 consecutive days)

NEW

15 & 16 April 2020

Embedded Linux training

11 - 13 May 2020 (3 consecutive days)

25 – 29 May 2020 (5 consecutive days)

Electromagnetic compatibility – design techniques

Design patterns and emergent architecture

11 – 15 May 2019 (4,5 consecutive days)

26 – 29 May 2020 (4 consecutive days)

Thermal design and cooling of electronics workshop

Modern C++

12 – 14 May 2020 (3 consecutive days)

Starts 9 June 2020 (4 days in 2 weeks)

EMC course for mechatronic engineers

Secure coding in C and C++

19 June 2020 (1 day)

22 – 24 June 2020 (3 consecutive days)

Microelectromechanical systems

Object-oriented system control automation

29 June – 1 July 2020 (3 consecutive days)

Starts 17 September 2020 (2+3 consecutive days)

Design of analog electronics – analog IC design

SYSTEM

Starts 7 September 2020 (11 days in 18 weeks)

Digital signal processing

Design for manufacturing

Starts 7 September 2020 (17 weekly Monday evenings)

Passive damping for high tech systems 26 – 28 May 2020 (3 consecutive days)

Basics & design principles for ultra-clean vacuum 15 – 18 June 2020 (4 consecutive days)

Motion control tuning

22 – 26 June 2020 (5 consecutive days)

Thermal effects in mechatronic systems 23 – 25 June 2020 (3 consecutive days)

Experimental techniques in mechatronics 23 – 25 June 2020 (3 consecutive days)

Mechatronics system design – part 1

28 September – 2 October 2020 (5 consecutive days)

Multicore programming in C++ Starts 19 May 2020 (1 day)

Test and design-for-test for digital integrated circuits

MECHATRONICS

Starts 19 March 2020 (2 evening sessions)

Introduction to deep learning

16 – 17 April 2020 (2 consecutive days)

Starts 14 September 2020 (9 days in 16 weeks)

Software engineering for non-software engineers 23 - 25 March 2020 (3 consecutive days)

Ultra low power for Internet of Things

Design of analog electronics – analog electronics 1

Starts 27 October 2020 (15 weekly afternoons)

Object-oriented analysis and design – fast track

23 – 25 March 2020 (2,5 consecutive days) 6 – 8 April 2020 (3 consecutive days)

Applied optics in Eindhoven

SOFTWARE

Signal integrity of a PCB workshop EMC for motion systems

Expected in September 2020 (15 weekly morning sessions)

Starts 5 March 2020 (3 days + assurance session) NEW N TIO LOCA

System architect(ing) in Zwolle

23 – 27 March 2020 (5 consecutive days)

Value-cost ratio improvement by value engineering 16 & 17 April 2020 (2 consecutive days)

Introduction to SysML 16 April 2020 (1 day)

System requirements engineering improvement 11 & 12 May 2020 (2 consecutive days)

Introduction to deep learning 19 May 2020 (1 day)

Systems modelling with SysML

9 – 12 June 2020 (4 consecutive days)

System architect(ing) in Eindhoven 22 – 26 June 2020 (5 consecutive days)

O

pinion

INDUSTRIAL AUTOMATION Robert Howe is an independent management consultant.

Digitalization: act now, act fast

D

ue to Brexit, I recently became a Dutch citizen, a move I’d never expected to make. I was therefore rather surprised to feel very proud of my new passport and I’ve come to use it almost exclusively on my travels, not least because I get a kick out of strangers complementing me on my excellent English.

The Netherlands is a great country and I very much enjoy living here.

One of the greatest things about it is its burgeoning tech industry.

We have so many things going for us, from high tech multinationals to leading universities to a rapidly growing spirit of entrepreneurialism and a government that’s set on building a knowledge economy. It’s a very exciting time and place to be alive.

And yet, in spite of all the positives, we have the capacity to do better. In fact, we need to do better because we’re a small country competing on the global stage against fierce rivals with far greater resources.

The source of my concern is the lack of appreciation for the strategic importance of digitalization amongst executive and senior management in our industrial base. Without a doubt, the success of our region is also built upon our world-class software engineering skills. So how can it be that our top management fails to understand the strategic significance of software and digitalization?

The answer lies in the fact that for the last 30 years, innovation has been driven by advances in hardware and physical technologies, with software being seen as merely the glue that holds everything together. Today’s senior management were the pioneers of hardware-driven innova-

tion and, therefore, they can be forgiven for their perspective. But times are changing very quickly. More than half of the technologies listed on the Gartner Hype Cycle are software intensive. Digitalization is already a force majeure in business and consumer markets and has brought about radical change, including the emergence of software-centric companies such as Uber, Airbnb and Alibaba, and the destruction of companies such as Kodak and Nokia. It will be in industry, too. History tells

The benefits are market disrupting, without a doubt us that from 1900 to 1930, fully 40 percent of US manufacturers that existed at the turn of the century got wiped out because they failed to adapt quickly enough to the force majeure of electrification.

Executives who think they have time to take a wait-and-see approach to digitalization are making a big mistake. The fact is that it’s absolutely possible to be too late to the digitalization game. A recent report from McKinsey and the Global Lighthouse Network details the benefits that 4IR adopters are getting from digitalization. The numbers are ridiculous: 40 percent increase in manufacturing efficiency, 63 percent increase in workforce efficiency, 30 percent reduction in throughput time, and so on. These are market-disrupting benefits, without a doubt.

Imagine that your competitor increased their efficiency by 8 percent and reinvested those gains in further digitalization. Their advantage over your business would compound rapidly and by the time you noticed, it would be impossible for you to catch up. Your existing margins wouldn’t support the massive level of investment required. And even if they could, it’s unlikely that your organization could move quickly enough.

After all, you were slow to catch on to the significance of digitalization in the first place, so your organization is probably laggardly by nature.

Fortunately, the reverse is also true. Imagine if you started investing in digitalization immediately. Now, you have the advantage of rapidly compounding gains. By the time your competitors notice, you’ll have an unassailable lead. So, consider: if you act now on digitalization, the worst case is that you remain on par with your competitors. The best case is that you get an unassailable lead over them.

If you don’t act on digitalization, the worst case is that you’ll become irretrievably uncompetitive and slowly bleed business to your competitors.

There’s no best case for not acting since 4IR disruption is already a fact.

The good news is that we’re still in the early days of 4IR and, as McKinsey put it, “Leadership in digital manufacturing is open to anyone willing to commit to it.” But this will change quickly. The compound gains to be had from digitalization will rapidly create a widening gap between the smart and the slow. There’s really only one way to address the challenge of digitalization: act now, act fast.

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B a c kg r o u n d

Software engineering

Coaching in the third wave of Agile “We’ve mastered the Agile way of working. The teams will continue doing their work, whether you coach them or not.” I just started a new assignment as an Agile coach and did my new manager just tell me that I wasn’t needed?

Derk-Jan de Grood

A

gile has been around for many years and some organizations are pretty far in adopting the Agile practices and mindset. Others are just starting and many are struggling to make it work. Analyzing the various organizations I’ve worked with, I distinguish three separate waves, each with its own challenges and scope. The waves overlap each other and each wave builds on its predecessor.

During the first wave, the main focus is on teaching Agile and training the teams.

As progress declines, the second wave ar46

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rives, shifting the focus to cross-team alignment. There might be a small downfall in the perceived maturity as teams need to reorganize and adapt their way of working to align with the other teams, but this will enable a next growth spurt. The third wave comes with teams building a technical product and changing their focus to delivering value and boosting business performance. Again, there might be a downfall in maturity as more people in the enterprise get involved, but this will enable a further acceleration in business delivery.

Understanding and recognizing the waves of an Agile journey helps to grasp the different perceptions organizations have and enables us to tailor our coaching approach. The first reviewers of this article started to map their organization on the waves and spontaneously indicated which practices were already in place and what they needed to work on. I, therefore, believe it will enable us to have a better grip on Agile transformations and help us to explain why we focus on certain aspects. It puts our interventions in perspective and

provides a roadmap for the organization. It also offers insight into how the role of an Agile coach is developing.

The first wave: focus on teaching Agile and training the teams

Most organizations starting with Agile adopt Scrum or Kanban. Management plays a crucial role during the transformation. They’ll need to explain to their teams why they chose for an Agile way of working and how this has an impact on what’s expected from everyone. Agile coaches work with management so they can explain the impact of Agile and support the change.

But the teams are the foundation for the Agile development process. If their core activity – realizing IT solutions – is hampered, this will obstruct the later phases as well. The first wave is therefore characterized by a strong focus on teaching Agile and training the teams.

During the introductory phase, the Agile teams get trained and are accompanied by an Agile coach while doing their daily work.

The transition to Scrum is a learning process. By doing they will learn what it’s like to work together in short iterations, create transparency and continuously improve themselves. In some respects, the Agile way of working differs greatly from traditional development, with different roles and responsibilities. The product owner and scrum master roles will need to be clearly defined so they’re understood by everyone.

The teams are the foundation of the first wave of Agile. When they’ve shown that they can deliver completed backlog items and that they’re capable of selfimprovement, the first wave naturally evolves into a new phase. This doesn’t mean that Agile coaches should ignore the teams, since these will still need attention and guidance. But when the individual teams hit their stride, more impact is achieved by looking at the way they collaborate.

The second wave: cross-team alignment

In the second wave, the adoption of Agile is shifting from a single-team focus to a wider organizational approach. Organizations increasingly start to understand that business agility and responsiveness are key to survive and stay ahead of the competition.

Maturity

Agile maturity models are often used to measure the progress of the transformation.

For instance, measuring how well the scrum events are adopted by the teams gives an indication of progress. The adoption of Agile practices can also help teams to selfreflect and self-improve.

In daily practice, we see that these kinds of measurements often lead to teams performing the events without enthusiasm and understanding of their purpose. We encounter teams successfully fulfilling all the requirements for a high maturity level but failing to understand the Agile mindset. “Our teams are repeating the dance steps,” a manager sighed, “but they don’t dance to the music.”

Another effect we encounter is that teams often fall back in maturity. With the exception of one or two champion teams, maturity often gets stuck at an average of 2 or 3 on a scale of 5. This decline can be caused by workload and a lack of understanding of the value of the events. Another factor is changing team compositions.

New team members are hired that didn’t have the same training and teams get a new product owner or scrum master. Logically, teams are likely to fall back in maturity a bit. Although the focus of the coaching changes in the second wave, attention to what happens in the teams will be necessary.

In order to yield value, the work of single Agile teams should, therefore, be integrated and embedded in larger business processes.

During this phase, we pay less attention to the output of single teams, but rather start thinking in releases. We can call these technical products or minimal viable products.

In order to organize the work, all teams engage in portfolio planning. The aim is to align the teams and to start working on a collective goal. The focus on workable releases can be enhanced by having a chief product owner or leader to help the product owners prioritize and see the bigger picture.

In organizations employing the Scaled Agile Framework (Safe), this is done in so-called program increment planning sessions, in which all the teams gather to plan their work for the following six sprints. In noneSafe organizations, we often see the product owners of the different teams reconvene at a portfolio marketplace. In both settings, the aim is the same: set a release goal and translate it into more detailed work items, reduce the team interdependencies or at least make them transparent so that the work of the various teams can be combined and integrated into one single solution.

Restructuring the teams will reduce dependencies, enabling a more predictable flow of product releases. A performance dialogue will stimulate teams to help each other and frequently integrate their work.

Although this seems obvious, I still en-

counter many organizations with teams that don’t share the same objectives and have their own priorities. Helping another team on its most important item is therefore not always high on their own sprint backlog. A collective program board, review sessions with other teams and the involvement of the business stakeholders can boost the focus on collaboration and collective ownership.

Whereas the Agile transformation during the first wave focused on teaching the teams to adopt Agile practices, managers of organizations in the second wave will experience that their role is changing. They might have been used to project management-like roles and strong involvement in both content and planning. Now, they’re expected to facilitate and lead rather than manage the teams. In this phase, discussions will arise about the role of management in relation to that of the product owner and the scrum master – most likely resulting in more autonomy for the teams that embrace the Agile values and mindset.

The second wave is a difficult phase. Individual teams will get less attention as the Agile coach is focusing on cross-team challenges. Still, coaches might spend some time ensuring that the organization can train new people and teams don’t fall back in maturity.

Coaching in the second wave is aimed at defining clear release goals. On the release level, there’s a need for portfolio meetings, stake1 47

B a c kg r o u n d

Software engineering Typical challenges per wave

Wave 1

Wave 2

Wave 3

Agile roles

Defining and helping the product owner and scrum master to fill in their role effectively

Discussing the management role in relation to product owner and scrum master role

Empowering leadership with management

Collaboration

Stimulating interdisciplinary collaboration within the team

Stimulating the product owner and team members of different teams to align, help and integrate their work

Stimulating cross-team collaboration with a focus on delivering a business product

Dependencies

Discussing dependencies between user stories and optimizing work during sprints

Making team interdependencies transparent and part of the portfolio planning

Making cross-product dependencies transparent and part of the commercial release planning

Governance

Measuring Agile maturity of the teams based on the execution of events

Measuring Agile maturity based on the adoption of Agile values and team autonomy

Measuring Agile maturity based on quality, predictability and delivered value

Organizational/ team structure

Forming teams based on history or required skills

Reorganizing teams into feature teams

Reorganizing teams around customer journeys

Quality

Ensuring all user stories are tested according to the DoD

Organizing integration and end-to-end testing on the integrated system

Measuring the perceived quality (of the business solution)

Release planning

Planning sprints with the teams to work on the most important items

Engaging overall portfolio planning with the product owners of the teams, thinking in minimal viable products/ releases

Drafting an organizational roadmap and defining minimal viable products with business stakeholders, focusing on business value

Review and demo

Organizing reviews for the teams and involving stakeholders

Organizing collective review sessions with other teams and involving business stakeholders

Reviewing and demoing the completed features and epics with respect to the business goals defined for the minimal viable product

Performance metrics

Completion of sprint goals

Completion of release or quarterly goals

Business KPI based on delivered value, net promoter score, compliancy

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The three phases of Agile. The challenges and scope change as Agile maturity grows over time.

holder involvement and a test approach.

Coaches can moderate role discussions and accompany management in its search for a new leadership role. The second wave is followed by the third once teams have learned to plan and launch collectively built releases and focus shifts from realizing technical products to business delivery.

The third wave: business focus

In the third wave, organizations revisit their business focus. Agile never is a goal in itself, so it’s good to challenge the impact of the adoption. Does the way of working yield better quality, a shorter time-to-market and more delivered value? The performance dialogue changes once more and will be business aligned rather than release focused.

After all, it’s not about the release, it’s about business impact.

By now, management will need to show agile leadership. They need to lead the way by explaining the strategic themes, defining the business aim of the next release and helping the product owners to prioritize. In order to do this right, the IT organization needs a clear definition of its commercial products. Some organizations have this in place, but many still have a systems way of thinking. Product and customers aren’t well defined. Once the products are defined, their relative customer value can be determined.

Release planning should be based on the value and include cross-product dependencies.

Dependencies outside the organization may lead to delays or introduce inefficiencies.

Leadership should stimulate raising these impediments and take an active role in eliminating them. Most organizations I work with have too much on their plate.

When product owners indicate that their teams are unable to deliver the requested or expected epics, it’s up to the leaders to take action. No matter how difficult this may be in the corporate culture, they should address this at their level. This implies that in the third wave, higher management needs to adopt the Agile way of working as well.

In the third wave, the Agile coach is working closely with the leaders. Giving them feedback on their leadership style, addressing concerns and protentional problems. The coach helps setting up the performance dialogue and defines the appropriate metrics for key characteristics such as value and predictability. He or she facilitates design sprints at various levels in the organization to align the product definition and view on customer value.

Root cause analysis sessions can be held with the teams to optimize the flow and get bottlenecks on the table. Not seldom

Call to action

In which wave do you think your organization is? Do you recognize the challenges that are described in this article? Please share your thoughts and experiences with Derk-Jan at d.degrood@squerist.nl.

this will yield insights into process flows and team dynamics that can be improved.

Once again, the impact will be addressed on a higher corporate level and involve business management as well.

Note that there’s still a need for team coaching and teaching the basics. Teams tend to fall back in maturity. On top of that, the demands put on them change and the product owner and scrum master roles evolve as the organization transforms into a more agile enterprise. Even in the third wave, Agile coaches will need to guide the teams to grow their Agile maturity and business impact. However, we also see new roles emerge for them: in the third wave, they’ll act like counselors and business consultants with a strong focus on empowering leadership and optimizing business value. Since only a few organizations are already in the third wave, these roles aren’t clearly defined yet, but I believe they will shape the Agile coach of tomorrow.

Derk-Jan de Grood works as an Agile transition coach for Squerist. As a consultant, he helps organizations with their Agile transformation and embedding quality.

He’s an experienced trainer and he wrote several successful books. In 2016, he published “Agile in the real world – Starting with Scrum”. On his blog djdegrood.wordpress.com, he shares his knowledge and experience for everyone to benefit.

Edited by Nieke Roos

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BITS&CHIPS

INDUSTRIAL 5G CONFERENCE

8 APRIL 2020 IGLUU EINDHOVEN INDUSTRIAL5G.COM

#BCI5G

O

pinion

INDUSTRIAL AUTOMATION Biba Visnjicki is managing director at the Fraunhofer Project Center, an applied research center that focusses on production technologies, processes and digital solutions in manufacturing.

Are we prepared for disruptive times?

I

n March last year, Volkswagen CEO Herbert Diess posted a selfie on Linkedin with Jeff Bezos. Diess wrote that he was “looking forward to shaping the future” with his Amazon friend. In the same month, the world’s biggest carmaker and “The Everything Store” announced that they would create an industrial cloud for Volkswagen’s 122 facilities, with the intention of making the platform available to its worldwide ecosystem.

The carmaker’s long-term ambition is to integrate its supply chain – more than 30,000 locations of over 1,500 suppliers and partners – in an industrial cloud that spans the globe.

Two weeks later, BMW and Microsoft launched a similar initiative with their Open Manufacturing Platform.

It’s clear: we’re witnessing the emergence of production platforms that will strongly impact manufacturing and logistics processes. For some, this might sound futuristic and far away, but it’s actually happening now. Complete production lines, sites and systems will be interconnected via production platforms.

What does it actually mean?

In the short term, organizations should ensure that the digital architecture of their production environment – the digital backbone – is enabled for optimal data governance and autonomous operations. In the longer term, they have to define their own digital strategies that will steer them towards new organizational structures, manufacturing processes that are flexible in scale and scope and a redefined role in the digital supply chains.

The impact of the European automotive industry is a good example.

The ambition is clear. For instance, the Volkswagen group, which includes Audi, Porsche and Skoda, sold only 40,000 electric cars in 2018 – 0.4 percent of its total deliveries. The figure rises to just 100,000 – 0.9 percent – when plug-in hybrids are included.

Another newcomer in the production environment is 5G The target is 22 million over the next decade – an increase of almost 50 percent from its previous goal. This means that the challenges for suppliers regarding the scale are enormous.

In addition, there will be a drastic change in the needs of automotive OEMs. Electric vehicles do not need water-based engine cooling or exhaust systems. Only 17 percent of the mechanical parts in cars with combustion engines will be the same as in electric cars. These kinds of developments in the automotive industry will impact the Netherlands strongly. We are a country of first, second and third-tier suppliers. The supply demands and supply chains will go through serious reconfiguration. We expect that the pool of preferable suppliers will soon be based on new values, viz social and environmental responsibilities and digital capabilities providing flexibility in supply scale and scope.

Another newcomer in the production environment is 5G. It provides low latency and high reliability to support critical applications: real-time

monitoring and diagnostics, control of manufacturing processes, mobile service robots, autonomous transport, product identification, to name a few. 5G will not only improve the speed and reliability of communication but will also provide a means for energy reduction and bring other environmental benefits. In some cases, an 80 percent energy reduction is to be expected with 5G. Every organization, regardless of its size or industry branch, is dealing with the transition to a digital environment. At Fraunhofer, we’re working with many companies, supporting them to find the best route in these disruptive times.

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NEWS 5G

Eindhoven startup Maxwaves beams 5G to the max The release of the next-gen 5G network is just around the corner.

But to realize the enormous potential of the upgraded network, an overhaul of the infrastructure is still required. A new Eindhovenbased startup Maxwaves believes its solution can pick up the slack and beam high-frequency signals over longer distances.

Collin Arocho

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“Another main feature of our product is its capability to combine and focus the radio frequency energy in the air, also known as spatial power combining. If more power is needed, we activate more of the small antennas in the focal plane. That way we can increase the effective isotropic radiated power – the EIRP, which measures the radio frequency power in the air. That’s a real point of distinction.”

Credit: Maxwaves

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here are still several unknowns about the highly anticipated arrival of 5G. As 5G is expected to move to higher frequencies for enhanced data rates, the questions become: how far can the signals travel? What effect will weather have on propagation? And perhaps most importantly, what’s the much-needed infrastructure overhaul going to cost?

Ronis Maximidis, of Eindhoven-based startup Maxwaves, believes he has a costefficient, low-power solution, thanks to the development of devices with several small antennas (pixels) at the focal plane of a reflector – known as a focal-plane array (FPA). The technology was developed by fellow-student and contributor Ali AlRawi during his PhD studies at Eindhoven University of Technology.

The Maxwaves solution is a new antenna system that combines a high-gain reflector and a small sub-reflector positioned around the focal point. At the center of the larger reflector sits a constellation of small antennas that enable power spreading across the elements of the FPA, resulting in enhanced beam-steering capabilities.

“This configuration lets us place all the electronics behind the reflector and use it as a heat sink. Millimeter-wave electronics are highly inefficient and generate a lot of heat, by using adaptive power control we can reduce the wasted system power, which helps in terms of sustainability,” explains Maxwaves CEO Maximidis.

The technology was developed by fellowstudent and contributor Ali Al-Rawi during his PhD studies at Eindhoven University of Technology.

Low cost, low power

Other conventional systems, like classical antenna arrays, can achieve similar beam-steering capabilities, however, those systems utilize hundreds, or even thousands, of active antenna elements. This results in high power consumption and subsequently higher cost. Maxwaves opted for a more economical approach. “At high frequencies, high-power amplifiers are costly and inefficient,” says Al-Rawi. “Because of that, we chose to use low-power, silicon-based amplifiers, which are lower in cost.”

Another difference from traditional, reflector-based, systems is that the Maxwaves solution is electronically controlled and requires no special tooling, scopes or lasers when being installed. “There’s nothing too difficult or sophisticated in the deployment of Maxwaves’ solution,” comments Al-Rawi. “It’s as simple as pointing the transmitter in the direction of the receiver and then electronically manipulating the FPA based on the measurement readings. This makes it relatively simple to find the optimal point of connection and is much more accurate than trying to achieve the same connections mechanically.”

Environmentally resistant

According to the Maxwaves founder, one of the standout features of the system is its resilience to and effectiveness in bad weather – a big hurdle for any 5G network solution to overcome. Because 4G uses

Credit: Maxwaves

“By using adaptive power control we can reduce the wasted system power, which helps in terms of sustainability,” explains CEO Ronis Maximidis.

low-frequency channels, the signals can travel further and are more resistant to inclement weather. But as the next generation network moves toward the higher,

millimeter-wave frequencies, the distance that the signal can transmit is a real concern. Adding environmental factors, like bad weather, will only complicate the equation and further diminish the propagation.

“Rain and wind can really attenuate the signal in a significant manner. When using high-gain reflective antennas, that’s always going to be an issue,” explains Maximidis.

“We developed our focal-plane-array-fed antenna with automated alignment capabilities. This provides the system with the capacity to conduct limited scanning, allowing it to automatically counteract the wind’s twist-and-sway effect on antenna masts. Furthermore, our ability to adjust the power output means we can compensate for the adverse effects of rain, making the Maxwaves solution a viable option even in poor environmental conditions.”

Recently, Maxwaves took to the rooftops of the TUE campus to put its prototype to the test. Looking to validate its technology, Maximidis’ team placed a transmitter and receiver on the top of the Vertigo and

Spinoff or startup?

At its inception, Maxwaves was slated to be a spinoff from Eindhoven University of Technology. With a recent tweak in the system’s design, the company is no longer utilizing IP from the university and Maxwaves will now be launching as a startup.

Flux buildings, on opposite ends of the campus, and successfully linked the two using its electronically controlled beam-steering method. “These tests show that our FPA offers enhanced beam-steering capabilities for point-to-point links at longer distances,” boasts the CEO. “Typical market solutions can go about 5 km in ideal conditions. When the high-frequency waves are restricted, they shift back to the lower bands, but that significantly restricts the bandwidth. With our technology, we believe we can double that and hit 10 km, while still offering the 10 Gb/s speeds – in all weather.”

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ANALYSIS AUTOMOTIVE

Lightyears away from production Despite the wave of excitement behind Lightyear’s hybrid solar/electric vehicle prototype, the Helmondbased startup still has much to prove to deliver on promises of mass production. As the timeline continues to shrink, will the Lightyear One ever make it to the market?

Collin Arocho

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Credit: Lightyear

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ore than once my Dutch counterparts have pointed out that I’m strangely optimistic. I’m enthusiastic and can easily buy into what people in the high tech world are selling. Startups are my favorite. I’m a sucker for an old-fashioned underdog success story and I love to see innovators try to change the playing field. But as positive as I am, there are some startups that leave me with many more questions than answers, clouding my zeal with realism and more than just a hint of skepticism. A perfect example – Lightyear.

Yes, the Lightyear One has some impressive technology. Designers opted to construct the car using light-weight aluminum and carbon fiber, which significantly cuts down on weight, while still delivering a sporty look. The extremely low drag coefficient means the car can travel further with each charge. A hood, roof and trunk all made from highly efficient solar cells panels for supplemental charging – all very cool.

I get the hype. What’s not to like about a group of young entrepreneurs looking to paint the automotive industry green?

Since the unveiling of the Lightyear One in June of last year, the prototype car has been on a whirlwind tour of the globe, drumming up excitement, and more importantly, luring investors with promises to deliver the eco-friendly electric vehicle that can be charged by the sun into mass production and the market. But this David vs Goliath story seems to be missing something. Namely, results.

To start, the Lightyear One is not a solar car. It’s an EV with solar cells to provide some additional charge. As to how much this really contributes to the overall range is somewhat murky and hasn’t been substantiated with any real test or demonstration.

Considering the limited number of days with full sunshine in the Netherlands, it’s hard to

imagine that the solar panel offers a serious boost in charging – at least not in this area.

In ideal conditions, Lightyear claims its car can travel up to 725 km on a single charge of its modest 60 kWh battery.

The problem: what constitutes ideal? It’s more than just the presence of the sun and good weather. Using the heat or airconditioning? Connecting your phone and using the car’s infotainment system?

Already, that’s less than ideal. Essentially, by using any of the creature comforts that we’ve all become accustomed to and expect in our modern vehicles means you’re not getting the most out of the car.

Still not deterred? Great. But to get a ticket to ride the hype train of the Lightyear One, you’ll need to lay down a cool 120,000 euros. That’s right, if you want to drive the car lauded as the “Tesla Killer”, you’ll need to be ready to make a realestate-sized investment. This in contrast to the 2019 top-selling car in the Netherlands, Tesla’s Model 3, with a base model coming

in at just under 50,000 euros. Perhaps, the Lightyear team has seen the writing on the wall. Just recently the company said it wants to mass-produce a much cheaper second model to appeal to the average consumer. The price tag for that, 55,000 euros.

Cost and comforts aside, it’s time to get real about Lightyear’s ambitious production scheme. To date, it appears that the startup still only has one working prototype of the Lightyear One. That means that road, safety and crash testing, as well as the all-important certification from the authorities at RDW, might be on the horizon, but are nowhere near practice. Which means that seriously producing the EV for the market is even further away. Anything short of the backing from a billionaire solar-tech enthusiast, it’s not clear how Lightyear can transition from a nice story to a viable player in the auto industry. Perhaps as the R&D branch of one of the larger automakers, but not likely as a real manufacturer – at least not any time soon.

UPCOMING ISSUES

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Careers and leadership in high tech

Bits&Chips 2 | 1 May 2020

Bits&Chips 3 | 12 June 2020

Having an innovative idea is one thing; reaching target customers and achieving competitive advantage is another. That requires a solid go-to-market strategy.

This issue shines a light on the daunting task of turning a promising invention into a successful product.

The need for engineers is as high as ever. Beginners and upward movers have plenty of jobs from which to choose. In this issue, experienced experts from small firms to major corporations reveal what it’s like and how to get ahead working in the high tech industry.

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