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4 October 2023 Reconfigurable phase-change photonic platforms for fast and efficient in-memory computing
Nathan Youngblood
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Proceedings Volume PC12647, Active Photonic Platforms (APP) 2023; PC126470W (2023) https://doi.org/10.1117/12.2676236
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
Photonics information processing strategies offer the unique ability to perform analog computation with ultra-low latency and high efficiency. However, designing compact and reconfigurable photonic architectures which scale well is a challenge. The combination of nonvolatile optical materials (such as Ge2Sb2Te5) and integrated photonics is a promising approach which enables non-volatile optical memory on-chip with low drift, compact footprint, and high-speed readout. The first part of the talk will focus on using this photonic memory—together with wavelength division multiplexing and “in-memory” computing techniques—to enable high-speed matrix-vector operations for machine learning applications. The second half of this talk will cover methods for configuring these optical phase-change materials using electrical programming schemes, such as mixed-mode plasmonic memory and electro-thermal switching with on-chip microheaters.
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© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Nathan Youngblood "Reconfigurable phase-change photonic platforms for fast and efficient in-memory computing", Proc. SPIE PC12647, Active Photonic Platforms (APP) 2023, PC126470W (4 October 2023); https://doi.org/10.1117/12.2676236
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KEYWORDS
Optical computing
Reconfigurable computing
Computer programming
Data processing
Integrated photonics
Machine learning
Optical matrix switches
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