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Intelligent Line Monitoring Maximum productivity through an integrated and automated strategy

by Tom Pilon, IBM Microelectronics Division; Mark Burns, Verlyn Fischer, Matthew Saunders, KLA-Tencor

Maximizing the number of yielding parts per wafer while minimizing the cost to produce each part is the goal of any semiconductor fabricator. For this reason, considerable investment is placed on ramping yields and protecting them once they mature. The task for the semiconductor industry becomes more challenging as critical dimensions decrease, the number of process steps and their interdependence increase, and as throughput becomes an ever-demanding factor. The result is that as these changes occur in the production environment, yield engineers require larger volumes of intelligently collected data.

They also require the tools to adequately process that data and make responsive changes on the production line to ramp and protect yield.

The industry’s need for greater volumes of intelligently collected defect data is mirrored at the state-of-the-art 0.25 µm technology fabrication facility of IBM’s Microelectronics Division, which produces multiple memory and logic devices across a number of technologies. IBM recognized the need to have a system that would help solve yield problems at a reasonable cost, maximize fab productivity and offer the flexibility to make enhancements with the advances in technology and manufacturing capacity.

KLA-Tencor’s Intelligent Line Monitoring System (ILM) was installed to assess the effectiveness of such an integrated approach to yield management.

What is an intelligent line monitor?

ILM is an integrated set of defect inspection systems, automatic defect classification (ADC) systems, optical review tools, scanning electron microscope (SEM) defect review tools, and a defect database and analysis system (figure 1). An intelligent line monitor is used to monitor and diagnose process excursions, provide information necessary to

Figure 1. Intelligent line monitoring system flow.

ramp yields on new products or technologies, and provide information necessary to predict yields.

As a product flows through the manufacturing line, samples of wafers are pulled, fed into the ILM system, and returned to the production line. As the product travels through the ILM system, wafers are inspected and reviewed. Data are exchanged between the various components in the ILM system.

The ILM solution implemented at IBM is comprised of multiple KLA-Tencor 2132/35 defect inspection systems, each of which was equipped with IMPACT ADC Spring 1999

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Figure 2. Reduction in time spent on classifier training and verification.

software, scanning electron defect review microscopes with automatic defect location (ADL) capabilities such as the Amray 3800, off-line optical defect review stations such as the CRS-1010, 2552 data analysis stations, in-house operational systems, in-house analysis systems, and a Quest defect analysis system.

The fundamental difference between an integrated defect reduction system and a non-integrated tool set is that the integrated system leverages the capabilities of the point products through integration and automation to generate a maximally informative and cost-effective sample (termed as “smart sample”). Such integration helps provide maximum information about only those defects that most detract from yield. This type of sampling strategy provides the greatest impact toward improving chip yields and fabricator productivity.

Because SmartSampling™ is automated by the ILM system, data can be continuously collected as products move through the production line. SmartSampling provides information which allows a line monitoring system to optimally detect process excursions, predict yield, and assist in yield learning. It does this by providing the source, type, and quantity of defects for products and technologies at the various process levels.

Distillation processing is the key feature of an ILM system used to produce a smart sample. It does this by choosing defects on which to collect additional information based on the defects’ potential impact on yield and other current in-line information.

Since the installation of ILM less than a year ago, IBM has reallocated up to 40 percent of its review operators to other work. In addition, time spent on training and verifying classifiers has decreased with the implementation of ADC versus manual classification techniques (figure 2). This occurred because ADC training sets are fixed, whereas human memory and judgment is subjective and varies with each operator’s level of expertise and knowledge of the defect source, defect-kill potential, operator mood, and time during the week or shift.

Furthermore, IBM has recently negated its originally established need to increase their number of manual review stations by 43 percent.

Cycle Time Reduction The ILM solution reduced cycle time in three ways.

First, by decreased defect review times. ADC has been shown to require far less time to classify a defect than manual review (by as much as 66 percent). Second, by decreased queue times. By coupling wafer inspection and optical review, the queue time between these steps was completely eliminated. Queue time is the time wafers sit on a shelf in between the inspection and review steps and has been measured to be as much as 70 minutes (on average) when performing manual review.

Using manual review versus ADC review systems on a defect sample set showed that the time savings with the ADC system were significant (figure 3). Based on a gate-oxide classifier, cycle time was reduced by as much as 67 percent with in-line ADC versus off-line manual review.

The third way cycle time was reduced was with the use of a minimized sample size. While reduction in the time taken to process defects during review is an important contribution to decreased cycle times, the greatest benefit derived with the ILM solution is that the sample set can be smaller yet contain all the critical information. This is important because a sample that is too large and takes too long to measure can cost more in lost production than in lost yield. The ILM solution allows on-the-fly defect filtering, which decreases the impact of doing off-line optical and SEM review on

Improved productivity

Installation of the ILM system at IBM and its ability to intelligently sample the production line generated several measurable productivity improvements:

Reallocation of Resources By automating the optical review process, valuable resources can be reallocated from manual review activities (such as performing review, training, or verification) to other value-added processing tasks or higher-level yield improvement tasks.

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Figure 3. Cycle time for three 8" wafers, with 100 percent coverage, 0.62 µm pixel, and 100 defects classified per wafer.

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The addition of database components to IBM’s ILM system, and feature extensions that were made with the addition of ADC as an option to the defect inspector demonstrate the ILM system flexibility. Inspectors here have been matched on three product levels (at two inspection sensitivities each) to 95 percent while ADC systems have matched in the neighborhood of 80 percent on product wafers and up to 97 percent on a defect standard wafer (figure 5). In addition, the means to track and maintain ADC matching has been studied.

Figure 4. Cycle-time reduction based on smart sampling.

Increased Flexibility

ADC classifier extendibility across products and similar process levels for a given technology has limited the set-up time and classifier maintenance duties to ranges acceptable in a manufacturing environment. For example, without classifier extendibility, a fab running two technologies, two products each, and three via levels per product would require 12 classifiers. However, with classifier extendibility, that same fab would need to build only two ADC classifiers. At IBM, classifier extendibility has been shown to reduce classifier creation by 80 percent.

Modular design of the ILM system allows inspection systems to be swapped, feature extensions to be made or a system to be conveniently expanded and enhanced as demanded by shifts in technology and capacity requirements. The benefit of increased flexibility, which is especially important in a manufacturing environment, is improved and protected cycle time.

Component similarity of tools in the ILM system allows the user to run a product interchangeably through similar tools eliminating overheads associated with set-up and extensive recipe management for each individual tool.

Figure 6. Total detection delay for via-level excursion monitoring

total cycle time. This is especially important since SEM defect review is a costly inspection.

Figure 4 shows how the number of defects that are sent for ADC on optical review tools such as the Confocal Review Station and SEM defect review tools such as the Amray is reduced by using smart sampling techniques.

strategies.

For the inspection/ADC components in an ILM system to be interchangeable they must match. Matching requires that defects be identified equally well on one or more inspection tools and that defect classification calls be similar on one or more ADC review systems.

In other words, a lot placed at any inspector/ADC system will generate the same wafer map and review pareto as it would at any other inspector/ADC system.

Yield Protection and Enhancement The time to detect a yield-limiting process excursion is the sum of the beta risk, inspection time, and review time. The beta risk is the time that a process is out-ofcontrol but undetected and depends on a number of parameters, including production rates, line sampling strategy, and defect count statistics.

Data gathered on products at the via levels were used to calculate time-to-detection using manual defect classification (MDC) and ADC techniques (figure 6). The greatest contribution to detection time was the beta risk. In addition, it has been shown that review accuracy has a significant impact in reducing the beta risk contribution. The model that was used to make this calculation was developed by the Competitive Semiconductor Manufacturing (CSM) Automated Inspection Focus Study Research Group.

Figure 5. ADC matching performance.

Detection delay may also be represented in terms of revenue loss per hour. The total cost associated with an Spring 1999

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The two greatest contributions to the total revenue loss are the beta risk and cost of review (figure 7). Revenue loss due to excursions at the via levels has been reduced by 36 percent with the implementation of the ILM solution.

The solutions for future challenges

Figure 7. Revenue loss per hour for via-level excursion monitoring

The ILM system serves to improve productivity by limiting the number of wafers exposed to yield-limiting conditions, allowing valuable resources to be reallocated to value-added processing tasks and reducing sampling cycle times while maintaining the integrity of the sample data.

strategies.

excursion is the sum of five components. Beta risk is the lost revenue due to product failing because of an out-of-control situation. Inspection cost is associated with the cost to operate an inspection system. Review cost is associated with the cost to perform review. In this model, ADC review cost was rolled into the cost of inspection. Source identification is a measure of the cost to isolate the cause of a measured excursion, and fixing cost is associated with the cost of resolving the yield-limiting problem.

As the ILM solution matures, new features will be added which will further reduce time-to-results. For example adaptive sampling, singular integrated interfaces, central inspection and classifier creation and management, intelligent classifier builds, signature analysis, automated engineering analysis and decision making, parametric analysis, and ADC on SEM and laser-based inspection tools will become standard features necessary to keep pace with the increasing demands of the industry. circle RS#012

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