A method to improve the computational performance of nonlinear all—optical diffractive deep neural network model

Yichen Suna Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, China;b Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, ChinaView further author information

Mingli Dongb Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, ChinaCorrespondence[email protected]
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Mingxin Yub Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, ChinaCorrespondence[email protected]
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Lidan Lub Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, ChinaView further author information

Shengjun Liangb Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, ChinaView further author information

Jiabin Xiac School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei, ChinaView further author information

Lianqing Zhub Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, ChinaCorrespondence[email protected]
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A method to improve the computational performance of nonlinear all—optical diffractive deep neural network model

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A method to improve the computational performance of nonlinear all—optical diffractive deep neural network model

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