[1] ZHOU ZH P, XU P F, DONG X W. Computing on silicon photonic platform[J]. Chinese Journal of Lasers, 47, 0600001(2020).

[2] WANG ZH J. Progress in optical computing technology[J]. Bulletin of Chinese Academy of Sciences, 2, 205(1987).

[3] ZHOU H Q, HUANG L L, WANG Y T. Deep learning algorithm and its application in optics[J]. Infrared and Laser Engineering, 48, 1226004(2019).

[4] FELDMANN J, YOUNGBLOOD N, WRIGHT C D et al. All-optical spiking neurosynaptic networks with self-learning capabilities[J]. Nature, 569, 208-214(2019).

[5] RÍOS C, YOUNGBLOOD N, CHENG Z G et al. In-memory computing on a photonic platform[J]. Science Advances, 5, eaau5759(2019).

[6] SHASTRI B J, TAIT A N, FERREIRA DE LIMA T et al. Photonics for artificial intelligence and neuromorphic computing[J]. Nature Photonics, 15, 102-114(2021).

[7] XIE Y W, ZHANG T, DAI D X. Applications of intelligent reconfigurable silicon photonic devices and circuits[J]. ZTE Technology Journal, 26, 64-69(2020).

[8] WANG J, YANG X F. Research progress and prospects of photonic chips[J]. World Science, 29-31(2020).

[9] CLEMENTS W R, HUMPHREYS P C, METCALF B J et al. Optimal design for universal multiport interferometers[J]. Optica, 3, 1460-1465(2016).

[10] ZOU W W, MA B W, XU SH F et al. Towards an intelligent photonic system[J]. China Information Sciences, 63, 160401(2020).

[11] [11] MARQUEZ B A, HUANG CH R, PRUCNAL P R, et al. Neuromphic Silicon Photonics f Artificial Intelligence [M]LOCKWOOD D J, PAVESI L. Silicon Photonics IV. Cham: Springer, 2021: 417447.

[12] ZHENG Y ZH, DAI J, ZHANG T. Multimodal feature fusion based on heterogeneous optical neural networks[J]. Chinese Optics, 16, 1343-1355(2023).

[13] GIAMOUGIANNIS G, TSAKYRIDIS A, MORALIS-PEGIOS M et al. Universal linear optics revisited: new perspectives for neuromorphic computing with silicon photonics[J]. IEEE Journal of Selected Topics in Quantum Electronics, 29, 6200116(2023).

[14] BANDYOPADHYAY S, HAMERLY R, ENGLUND D. Hardware error correction for programmable photonics[J]. Optica, 8, 1247-1255(2021).

[15] WILLIAMSON I A D, HUGHES T W, MINKOV M et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks[J]. IEEE Journal of Selected Topics in Quantum Electronics, 26, 7700412(2020).

[16] SPALL J, GUO X X, BARRETT T D et al. Fully reconfigurable coherent optical vector–matrix multiplication[J]. Optics Letters, 45, 5752-5755(2020).

[17] XIE F, ZHU SH L, ZHANG ZH R. Design of an optical power splitter with adjustable split ratio[J]. Chinese Optics, 16, 1121-1128(2023).

[18] ZHANG H, GU M, JIANG X D et al. An optical neural chip for implementing complex-valued neural network[J]. Nature Communications, 12, 457(2021).

[19] SHOKRANEH. F, NEZAMI M S, LIBOIRON-LADOUCEUR O. Theoretical and experimental analysis of a 44 recongurable MZI-based linear optical processor[J]. Journal of Lightwave Technology, 1(2021).

[20] SHOKRANEH F, GEOFFROY-GAGNON S, LIBOIRON-LADOUCEUR O. The diamond mesh, a phase-error- and loss-tolerant field-programmable MZI-based optical processor for optical neural networks[J]. Optics Express, 28, 23495-23508(2020).

[21] TSAKYRIDIS A, GIAMOUGIANNIS G, TOTOVIC A et al. Fidelity restorable universal linear optics[J]. Advanced Photonics Research, 3, 2200001(2022).

[22] SHI Y, REN J Y, CHEN G Y et al. Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks[J]. Nature Communications, 13, 6048(2022).

[23] SHAO R, ZHANG G, GONG X. Generalized robust training scheme using genetic algorithm for optical neural networks with imprecise components[J]. Photonics Research, 10, 1868-1876(2022).

[24] HAMERLY R, BANDYOPADHYAY S, ENGLUND D. Asymptotically fault-tolerant programmable photonics[J]. Nature Communications, 13, 6831(2022).

[25] RECK M, ZEILINGER A, BERNSTEIN H J et al. Experimental realization of any discrete unitary operator[J]. Physical Review Letters, 73, 58-61(1994).