Integrated diffractive optical neural network with space-time interleaving  

作　　者：符庭钊 黄禹尧 孙润 黄泓皓 刘文灿 杨四刚 陈宏伟 Tingzhao Fu;Yuyao Huang;Run Sun;Honghao Huang;Wencan Liu;Sigang Yang;Hongwei Chen(Beijing National Research Center for Information Science and Technology,Department of Electronic Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]Beijing National Research Center for Information Science and Technology,Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Chinese Optics Letters》2023年第9期84-90,共7页中国光学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(NSFC)(No.62135009);the Beijing Municipal Science&Technology Commission,Administrative Commission of Zhongguancun Science Park(No.Z221100005322010)。

摘　　要：Integrated diffractive optical neural networks(DONNs)have significant potential for complex machine learning tasks with high speed and ultralow energy consumption.However,the on-chip implementation of a high-performance optical neural network is limited by input dimensions.In contrast to existing photonic neural networks,a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed,high-dimensional,and all-optical input signal modulation.To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation,an on-chip DONN with a designed footprint of 0.0945 mm~2is proposed to resolve the vowel recognition task,reaching a computation speed of about 1.4×10^(13)operations per second and yielding an accuracy of 98.3%in numerical calculation.In addition,the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally.This method can realize the on-chip DONN with higher input dimension and lower energy consumption.

关 键 词：integrated diffractive optical neural networks machine learning arrayed waveguides 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]