Optical neural networks are emerging as a competitive alternative to their electronic counterparts,offering distinct advantages in bandwidth and energy efficiency.Despite these benefits,scaling up on-chip optical neur...Optical neural networks are emerging as a competitive alternative to their electronic counterparts,offering distinct advantages in bandwidth and energy efficiency.Despite these benefits,scaling up on-chip optical neural networks for end-to-end inference is facing significant challenges.First,network depth is constrained by the weak cascadability of optical nonlinear activation functions.Second,the input size is constrained by the scale of the optical matrix.Herein,we propose a scaling up strategy called partially coherent deep optical neural networks(PDONNs).By leveraging an on-chip nonlinear activation function based on opto-electro-opto conversion,PDONN enables network depth expansion with positive net gain.Additionally,convolutional layers achieve rapid dimensionality reduction,thereby allowing for an increase in the accommodated input size.The use of a partially coherent optical source significantly reduces reliance on narrow-linewidth laser diodes and coherent detection.Owing to their broader spectral characteristics and simpler implementation,such sources are more accessible and compatible with scalable integration.Benefiting from these innovations,we designed and fabricated a monolithically integrated optical neural network with the largest input size and the deepest network depth,comprising an input layer with a size of 64,two convolutional layers,and two fully connected layers.We successfully demonstrate end-to-end two-class classification of fashion images and four-class classification of handwritten digits with accuracies of 96%and 94%,respectively,using an in-situ training method.Notably,performance is well maintained with partially coherent illumination.This proposed architecture represents a critical step toward realizing energy-efficient,scalable,and widely accessible optical computing.展开更多
基金supported by the Fundamental Research Funds for the Central Universities.
文摘Optical neural networks are emerging as a competitive alternative to their electronic counterparts,offering distinct advantages in bandwidth and energy efficiency.Despite these benefits,scaling up on-chip optical neural networks for end-to-end inference is facing significant challenges.First,network depth is constrained by the weak cascadability of optical nonlinear activation functions.Second,the input size is constrained by the scale of the optical matrix.Herein,we propose a scaling up strategy called partially coherent deep optical neural networks(PDONNs).By leveraging an on-chip nonlinear activation function based on opto-electro-opto conversion,PDONN enables network depth expansion with positive net gain.Additionally,convolutional layers achieve rapid dimensionality reduction,thereby allowing for an increase in the accommodated input size.The use of a partially coherent optical source significantly reduces reliance on narrow-linewidth laser diodes and coherent detection.Owing to their broader spectral characteristics and simpler implementation,such sources are more accessible and compatible with scalable integration.Benefiting from these innovations,we designed and fabricated a monolithically integrated optical neural network with the largest input size and the deepest network depth,comprising an input layer with a size of 64,two convolutional layers,and two fully connected layers.We successfully demonstrate end-to-end two-class classification of fashion images and four-class classification of handwritten digits with accuracies of 96%and 94%,respectively,using an in-situ training method.Notably,performance is well maintained with partially coherent illumination.This proposed architecture represents a critical step toward realizing energy-efficient,scalable,and widely accessible optical computing.
