Non-volatile and reconfigurable on-chip optical digital neural network based on Sb2Se3-assisted phase shifters

Qiaomu Hu; Chu Wu; Jingyu Zhao; Yuexing Su; Minming Zhang

doi:10.1117/12.2687525

28 November 2023 Non-volatile and reconfigurable on-chip optical digital neural network based on Sb₂Se₃-assisted phase shifters

Qiaomu Hu, Chu Wu, Jingyu Zhao, Yuexing Su, Minming Zhang

Author Affiliations +

Proceedings Volume 12773, Nanophotonics and Micro/Nano Optics IX; 127731A (2023) https://doi.org/10.1117/12.2687525
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Programmable integrated photonics is one of the most promising hardware acceleration schemes for deep learning. The programing method based on phase change materials makes the device non-volatile but reduces the resolution, which may result in a loss of performance. Here we propose an on-chip optical neural network implemented with a cascaded array of 1:1 couplers and waveguides with a digital non-volatile phase shifter. The Sb₂Se₃-assisted phase shifters can achieve a phase shift from 0 to 2π in steps of π/16 through 5 independent non-volatile reconfigurable units. We also proposed a Digital-Aware-Training regime to train this digital model. The network achieves 100% and 83.7% accuracy in the recognition of 4 Latin letters and iris project, respectively. Compared with conventional reconfigurable schemes, this computing platform has the characteristics of non-volatile, low power consumption, and high modulation robustness.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiaomu Hu, Chu Wu, Jingyu Zhao, Yuexing Su, and Minming Zhang "Non-volatile and reconfigurable on-chip optical digital neural network based on Sb₂Se₃-assisted phase shifters", Proc. SPIE 12773, Nanophotonics and Micro/Nano Optics IX, 127731A (28 November 2023); https://doi.org/10.1117/12.2687525

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