Two-photon nanolithography of micrometer scale diffractive neural network with cubical diffraction neurons at the visible wavelength  

作　　者：Qi Wang Haoyi Yu Zihao Huang Min Gu Qiming Zhang 王祺;蔚浩义;黄梓浩;顾敏;张启明(Institute of Photonic Chips,University of Shanghai for Science and Technology,Shanghai 200093,China;Centre for Artificial-Intelligence Nanophotonics,School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]Institute of Photonic Chips,University of Shanghai for Science and Technology,Shanghai 200093,China [2]Centre for Artificial-Intelligence Nanophotonics,School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China

出　　处：《Chinese Optics Letters》2024年第10期93-99,共7页中国光学快报（英文版）

基　　金：supported by the National Key Research and Development Program of China(Nos.2021YFB2802000 and 2022YFB2804301);the Science and Technology Commission of Shanghai Municipality(No.21DZ1100500);the Shanghai Municipal Science and Technology Major Project,the Shanghai Frontiers Science Center Program(2021–2025 No.20);the National Natural Science Foundation of China(Nos.61975123,62305219,and 62205208);the Shanghai Natural Science Foundation(No.23ZR1443200);the China Postdoctoral Science Foundation(Nos.2022M712138 and 2021M702192);the Shanghai Super Postdoctoral Incentive Scheme(Nos.5B22904002 and 5B22904006)。

摘　　要：Free-space diffractive neural networks(DNNs)have been an intense research topic in machine learning for image recognition and encryption due to their high speed,lower power consumption,and high neuron density.Recent advances in DNNs have highlighted the need for smaller device footprints and the shift toward visible wavelengths.However,DNNs fabricated by electron beam lithography,are not suitable for microscopic imaging applications due to their large sizes,and DNNs fabricated by two-photon nanolithography with cylindrical neurons are not optimal for visible wavelengths,as the highorder diffraction could induce low diffraction efficiency.In this paper,we demonstrate that cubical diffraction neurons are more efficient diffraction elements for DNNs compared with cylindrical neurons.Based on the theoretical analysis of the relationship between the detector area sizes and classification accuracy,we reduced the size of DNNs operating at the wavelength of 532 nm for handwritten digit classification to micrometer scale by two-photon nanolithography.The DNNs with cubical neurons demonstrated an experimental classification accuracy(89.3%)for single-layer DNN,and 83.3%for two-layer DNN with device sizes similar to that of biological cells(about 100μm×100μm).Our results paved the pathway to integrate 3D micrometer-scale DNNs with microscopic imaging systems for biological imaging and cell recognition.

关 键 词：optical neural networks diffractive neural networks two-photon nanolithography 

分 类 号：O43[机械工程—光学工程] TP183[理学—光学]