Inverse-designed integrated all-optical nonlinear activators for optical computing

Abstract

Optical neural networks (ONNs) have been considered as an alternative solution to overcome the arithmetic and energy bottlenecks of electronic neural networks. However, the widespread implementation of ONNs is hindered by their lack of optical nonlinearity. In this work, three ultra-compact all-optical nonlinear activators are inverse-designed by combining the adjoint method and Kerr nonlinearity. The nonlinear response is mainly generated by the Kerr and the thermo-optic (TO) effect associated with the nonlinear refractive index. Transmission-as-computation and structure-as-function are realized, with a minimum activation threshold of 2.34 mW. In addition, we validated the feasibility and capability of the proposed method against benchmark machine learning tasks, in which the addition of nonlinear activation functions significantly improved the expressive power of the ONN, increasing the testing accuracy obtained from the Modified National Institute of Standards and Technology (MNIST) task from 88.15% to 93.25%. The proposed ONN framework with our nonlinear activators exhibited good robustness against phase errors in the network topology. We believe that this study contributes to the future development of large-scale chip-level ONNs.