Nathan Youngblood, PhD

1103 Benedum Hall | 3700 O’Hara Street | Pittsburgh, PA 15261 P: 412-383-7104

nathan.youngblood@pitt.edu www.pitt-photonics.github.io Assistant Professsor

Dr. Nathan Youngblood is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE). His research explores new methods for high-speed and low-energy computation by combining new discoveries in reconfigurable optical materials with innovative approaches to photonic integration. Before joining Pitt in 2019, Dr. Youngblood was postdoctoral fellow at the University of Oxford developing phase-change photonic devices.

Photonic Architectures for Ultrafast AI

Optics has the unique ability to perform linear operations with (theoretically) no energy consumption. This allows for high-speed, lowenergy matrix-vector multiplications in the optical domain. Since matrix multiplications form the basis for nearly all machine learning algorithms, hybrid hardware accelerators based on integrated photonics and electronics could significantly increase data throughput by leveraging the wavelength multiplexing and high modulation speeds of silicon photonics. We have already made significant contributions toward this goal by establishing the field of “in-memory photonic computing.” Borrowing from concepts developed for analog electronic accelerators, we have proposed (Science Advances, 2018) and demonstrated (Nature, 2021) the ability to perform multiple linear operations simultaneously in a photonic memory array at the speed of light, leading to a >1,000× improvement in compute density over state-of-the-art GPUs. Additionally, latency in our photonic accelerator is negligible, which would be transformative for applications requiring ultrafast AI such as high-speed qubit classification, plasma control in fusion reactors, real-time signal processing at radio frequencies, autonomous navigation, and ultrafast training of DNNs.

Relevant Publications

• N. Youngblood, C. Talagrand, B. Porter, et al. “Broadly-tunable smart glazing using an ultra-thin phase-change material,” ACS

Photonics 9(1), 90–100 (2022) • J. Feldmann*, N. Youngblood*, M. Karpov*, et al. “Parallel convolution processing using an integrated photonic tensor core,” Nature 589, 52–58 (2021) • N. Farmakidis*, N. Youngblood*, X. Li, et al. “Plasmonic nanogap enhanced phase change devices with dual electrical-optical functionality,” Science Advances 5(11), eaaw2687 (2019) • C. A. Rios*, N. Youngblood*, Z. Cheng, et al. “In-memory computing on a photonic platform,” Science Advances 5(2), eaau5759 (2018) • N. Youngblood, C. Chen, S. J. Koester, M. Li,

“Waveguide-integrated black phosphorus photodetector with high responsivity and low dark current,” Nature Photonics, 9, 249–252 (2015)

Waveguide-Integrated 2D Materials for High Performance Optoelectronics

The desirable optoelectronic properties of 2D materials such as graphene and black phosphorous, combined with their ability to be easily transferred to arbitrary substrates, make them a perfect candidate for integration with photonics. Placing 2D materials on top of a waveguide has the added benefit of overcoming the low out-of-plane absorption of these materials. Dr. Youngblood demonstrated the first waveguide-integrated black phosphorus photodetector, enabling the detection of telecommunications wavelengths with much higher efficiency than prior work using graphene (Nature Photonics, 2015). Our group is continuing to explore photonic integration of other novel 2D materials for modulation, detection, and storage of optical information.