Abstract

Matrix multiplication is a fundamental building block for modern information processing and artificial intelligence algorithms. Photonics-based matrix multipliers have recently attracted much attention due to their advantages of low energy and ultrafast speed. Conventionally, achieving matrix multiplication relies on bulky Fourier optical components, and the functionalities are unchangeable once the design is determined. Furthermore, the bottom-up design strategy cannot easily be generalized into concrete and practical guidelines. Here, we introduce a reconfigurable matrix multiplier driven by on-site reinforcement learning. The constituent transmissive metasurfaces incorporating varactor diodes serve as tunable dielectrics based on the effective medium theory. We validate the viability of tunable dielectrics and demonstrate the performance of matrix customization. This work represents a new avenue in realizing reconfigurable photonic matrix multipliers for on-site applications.

© 2022 Optica Publishing Group

Addressing data scarcity in optical matrix multiplier modeling using transfer learning

Ali Cem, Ognjen Jovanovic, Siqi Yan, Yunhong Ding, Darko Zibar, and Francesco Da Ros
Opt. Lett. 48(24) 6553-6556 (2023)

Reinforcement learning for W-band radio-over-fiber system using a polarization modulator

Jyun-Wei Li, Run-Kai Shiu, Ming-Han Wen, Yibeltal Chanie Manie, Zhi-Qi Yang, and Peng-Chun Peng
Opt. Lett. 47(8) 2008-2011 (2022)

Coherent optical neuron control based on reinforcement learning

Jun Zhang, Qiuquan Yan, Hengzhu Liu, Yuhua Tang, Tong Zhou, and Tian Jiang
Opt. Lett. 48(4) 1084-1087 (2023)

Fully reconfigurable coherent optical vector–matrix multiplication

James Spall, Xianxin Guo, Thomas D. Barrett, and A. I. Lvovsky
Opt. Lett. 45(20) 5752-5755 (2020)

Mid-infrared spectral reconstruction with dielectric metasurfaces and dictionary learning

Benjamin J. Russell, Jasper J. Cadusch, Jiajun Meng, Dandan Wen, and Kenneth B. Crozier
Opt. Lett. 47(10) 2490-2493 (2022)