Nonlinear absorption of 2D materials and their application in optical neural networks

Abstract

In recent years, optical neural network (ONN) research has blossomed due to the outstanding advantage of energy consumption and computing property. Regrettably, nonlinear processing in the optical domain remains a huge challenge. The optical characteristics of 2D material, particularly related to saturable absorption (SA), have enabled nonlinear operation. Here, we discuss the SA models with various categories and their application in ONNs. A feedforward artificial neural network was built for handwritten digit recognition to illustrate the feasibility of SA features as nonlinear mapping. For comparison, ONNs without the assistance of the activation function were used as a benchmark to examine the capability of the nonlinear models. A simulation shows that the accuracy of digit classification ranged from 86% to 95%, depending on the nonlinearity of the mediums. This work offers an optical nonlinear unit selection guideline to explore ONNS.