人工神经网络
非线性光学
水准点(测量)
非线性系统
能源消耗
光学计算
前馈神经网络
电子工程
领域(数学分析)
计算机科学
计算机工程
工程类
人工智能
物理
数学
电气工程
数学分析
量子力学
地理
大地测量学
作者
Caihong Teng,Weijie He,Wen Du,Jiang Wu,Zhiming M. Wang
出处
期刊:Journal of The Optical Society of America B-optical Physics
[Optica Publishing Group]
日期:2023-04-20
卷期号:40 (8): 2007-2007
被引量:1
摘要
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.
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