Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability

查看全文 作　　者：Maoliang [1]Wei;Junying [1]Li;Zequn [2,3]Chen;Bo [4]Tang;Zhiqi [1]Jia;Peng [4]Zhang;Kunhao [1]Lei;Kai [1]Xu;Jianghong [2,3]Wu;Chuyu [1]Zhong;Hui [1]Ma;Yuting [2,3]Ye;Jialing [2,3]Jian;Chunlei [2,3]Sun;Ruonan [4]Liu;Ying [1]Sun;[1]Wei.E.I.Sha;Xiaoyong [5]Hu;Jianyi [1]Yang;Lan [2,3]Li;Hongtao [1]Lin

机构地区：[1]Zhejiang University,College of Information Science and Electronic Engineering,State Key Laboratory of Modern Optical Instrumentation,Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province,Hangzhou,China;[2]Westlake University,School of Engineering,Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province,Hangzhou,China;[3]Institute of Advanced Technology,Westlake Institute for Advanced Study,Hangzhou,China;[4]Institute of Microelectronics of the Chinese Academy of Sciences,Beijing,China;[5]Peking University,School of Physics,Frontiers Science Center for Nano-optoelectronics,State Key Laboratory for Mesoscopic Physics,Beijing,China

出　　处：《Advanced Photonics》索引2023年第5卷第4期,共9页

基　　金：supported by the National Key Research and Development Program of China (2019YFB2203002 and 2021YFB2801300);National Natural Science Foundation of China (62105287, 91950204, and 61975179);Zhejiang Provincial Natural Science Foundation (LD22F040002) 摘　　要：Optical neural networks (ONNs), enabling low latency and high parallel data processing withoutelectromagnetic interference, have become a viable player for fast and energy-efficient processing andcalculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, andhigh-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energyconsumption of phase-change material-based photonic memories make them inapplicable for in situ training.Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator,a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation wasdemonstrated. For the first time, a concept is presented for electrically programmable phase-changematerial-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zerostatic power consumption data processing in ONNs. ONNs with an optical convolution kernel constructedby our photonic memory theoretically achieved an accuracy of predictions higher than 95% when testedby the MNIST handwritten digit database. This provides a feasible solution to constructing large-scalenonvolatile ONNs with high-speed in situ training capability. 关键词：phase-change materials optical neural networks photonic memory silicon photonics reconfigurable photonics

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Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability

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