Polarization-encoded neural networks with simplified grating patch  

作　　者：Chengyan ZHONG Xiang WANG Lingfei LI Yuanchi CUI Lei XIAO Dawei SONG Junxiong GUO Wen HUANG Yufeng GUO Yu LIU 

机构地区：[1]College of Integrated Circuit Science and Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China [2]National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology,Nanjing 210023,China [3]College of Integrated Circuits Hangzhou Global Scientific and Technological Innovation Centre,Zhejiang University,Hangzhou 310027,China [4]Nanjing Mumusili Technology Co.,LTD,Nanjing 211100,China [5]School of Integrated Circuits,Tsinghua University,Beijing 100084,China [6]Institute of Advanced Study,School of Electronic Information and Electrical Engineering,Chengdu University,Chengdu 610106,China [7]State Key Laboratory of Electronic Thin Films and Integrated Devices,School of Integrated Circuit Science and Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China

出　　处：《Science China(Technological Sciences)》2025年第2期269-277,共9页中国科学(技术科学英文版)

基　　金：supported in part by the National Natural Science Foundation of China(Grant Nos.62371095,62201096,62401276);by the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(Grant No.NY223161);in part by the Jiangsu Provincial Key Research and Development Program(Grant No.BE2022126);the Key R&D Program of Sichuan Province(Grant No.2022ZHCG0041);the National Key Research and Development Program of China(Grant No.2022YFB3206100);the Natural Science Foundation of Sichuan Province(Grant No.2024NSFSC0509);the China Postdoctoral Science Foundation(Grant Nos.2024T170097,2024M760343).

摘　　要：Optical neural networks(ONNs)offer a promising solution for high-performance,energy-efficient artificial intelligence hardware by leveraging the parallelism and speed of light.However,the large-scale implementation of ONNs remains challenging due to the bulky footprint and complex control of optical synapses.In this work,we propose and simulate a plasmonic polarized synaptic architecture that overcomes the diffraction limit and enables ultra-compact ONNs.By tuning the polarization state of incident light,the optical transmittance through each plasmonic unit can be dynamically adjusted to represent a synaptic weight.Our plasmonic structures,with features as small as 40 nm,operate well below this limit in the visible spectrum(400-750 nm).Compared with diffraction and interference-based circuit designs,our proposed method achieves a substantial reduction in synaptic density by factors of 150000-fold and 1500-fold,respectively.Furthermore,we successfully demonstrate a proof-of-concept plasmonic ONN applied to the Canadian Institute for Advanced Research—10 classes(CIFAR-10)dataset using a Visual Geometry Group network with 16 layers(VGG16)model.After training for 80 epochs,the network achieves an accuracy of 93%.The polarization-tunable plasmonics paves the way towards scalable ONNs for next-generation artificial intelligence(AI)accelerators and smart sensors.

关 键 词：optical neural networks polarization dependent nanophotonic ultra-compact devices diffraction limit 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O436.3[自动化与计算机技术—控制科学与工程]