Wavelength tunable resonant phase-change synaptic weights for photonic neuromorphic computing

Yihao Cui; Behrad Gholipour

doi:10.1117/12.2633150

3 October 2022 Wavelength tunable resonant phase-change synaptic weights for photonic neuromorphic computing

Yihao Cui, Behrad Gholipour

Proceedings Volume 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; 1220406 (2022) https://doi.org/10.1117/12.2633150
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

The prohibitively large inclusions of micro-ring resonators, interconnected waveguide crossbar arrays, and multi-port multi-mode interferometers components demonstrated in the latest integrated photonic neural network hardware accelerators, pose a significant scaling issue for implementing large number of neural connections required to accurately represent the cognitive functions of a biological brain. In this investigation we utilize phase-change chalcogenide material GST to replicate a non-volatile synaptic weight with built-in memory functionality by employing metamaterial design principles for wavelength-division multiplexing photonic architectures. The transmission response of the optimized GST metamaterial gives rise to contrast ratios of 6dB in both positive and negative weighting values.

Conference Presentation

Citation Download Citation

Yihao Cui and Behrad Gholipour "Wavelength tunable resonant phase-change synaptic weights for photonic neuromorphic computing", Proc. SPIE 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, 1220406 (3 October 2022); https://doi.org/10.1117/12.2633150

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