Opto-Electronic Engineering, Volume. 51, Issue 7, 240101(2024)

Progress in the research of optical neural networks

Shuiying Xiang1,*... Ziwei Song2, Yahui Zhang1, Xingxing Guo1, Yanan Han1 and Yue Hao3 |Show fewer author(s)
Author Affiliations
  • 1State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, Shaanxi 710071, China
  • 2Fundamentals Department, Air Force Engineering University, Xi’an, Shaanxi 710051, China
  • 3State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi’an, Shaanxi 710071, China
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    References(144)

    [1] J McCarthy, M L Minsky, N Rochester et al. A proposal for the Dartmouth summer research project on artificial intelligence: August 31, 1955. AI Mag, 27, 12-14(2006).

    [2] Y LeCun, Y Bengio, G Hinton. Deep learning. Nature, 521, 436-444(2015).

    [3] G E Moore. Cramming more components onto integrated circuits. Electronics, 38, 114-117(1965).

    [4] C Mead. Neuromorphic electronic systems. Proc IEEE, 78, 1629-1636(1990).

    [5] K Amunts, C Ebell, J Muller et al. The human brain project: creating a European research infrastructure to decode the human brain. Neuron, 92, 574-581(2016).

    [6] T R Insel, S C Landis, F S Collins. The NIH BRAIN initiative. Science, 340, 687-688(2013).

    [7] C L Martin, M Chun. The BRAIN initiative: building, strengthening, and sustaining. Neuron, 92, 570-573(2016).

    [8] J Ngai. BRAIN 2.0: transforming neuroscience. Cell, 185, 4-8(2022).

    [9] H Okano, E Sasaki, T Yamamori et al. Brain/MINDS: a Japanese national brain project for marmoset neuroscience. Neuron, 92, 582-590(2016).

    [10] M M Poo. Whereto the mega brain projects?. Natl Sci Rev, 1, 12-14(2014).

    [11] M M Poo, J L Du, N Y Ip et al. China brain project: basic neuroscience, brain diseases, and brain-Inspired computing. Neuron, 92, 591-596(2016).

    [12] M M Poo, B Xu, T N Tan. Brain science and brain-inspired intelligence technolog—an overview. Bull Chin Acad Sci, 31, 725-736(2016).

    [13] T J Huang, L P Shi, H J Tang et al. Research on multimedia technology 2015——advances and trend of brain-like computing. J Image Graphics, 21, 1411-1424(2016).

    [14] S Y Xiang, Z W Song, S Gao et al. Progress and prospects of photonic neuromorphic computing (Invited). Acta Photonica Sin, 50, 1020001(2021).

    [15] E Painkras, L A Plana, J Garside et al. SpiNNaker: a 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J Solid-State Circuits, 48, 1943-1953(2013).

    [16] B V Benjamin, P R Gao, E McQuinn et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc IEEE, 102, 699-716(2014).

    [17] P A Merolla, J V Arthur, R Alvarez-Icaza et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345, 668-673(2014).

    [19] D Ma, J C Shen, Z H Gu et al. Darwin: a neuromorphic hardware co-processor based on spiking neural networks. J Syst Archit, 77, 43-51(2017).

    [20] M Davies, N Srinivasa, T H Lin et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro, 38, 82-99(2018).

    [23] Z S Liu, S Chen, P Y Qu et al. SUSHI: ultra-high-speed and ultra-low-power neuromorphic chip using superconducting single-flux-quantum circuits, 614-627(2023).

    [24] D Miller. Device requirements for optical interconnects to silicon chips. Proc IEEE, 97, 1166-1185(2009).

    [25] M A Nahmias, Lima T F De, A N Tait et al. Photonic multiply-accumulate operations for neural networks. IEEE J Sel Top Quantum Electron, 26, 7701518(2020).

    [28] J J Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci, 79, 2554-2558(1982).

    [29] J Liu, Q H Wu, X Sui et al. Research progress in optical neural networks: theory, applications and developments. PhotoniX, 2, 5(2021).

    [30] F C F Tsai, C J O’Brien, N S Petrović et al. Analysis of optical channel cross talk for free-space optical interconnects in the presence of higher-order transverse modes. Appl Opt, 44, 6380-6387(2005).

    [31] W H Hu, X J Li, J K Yang et al. Crosstalk analysis of aligned and misaligned free-space optical interconnect systems. J Opt Soc Am A, 27, 200-205(2010).

    [32] S Y Xiang, A J Wen, W Pan. Emulation of spiking response and spiking frequency property in VCSEL-based photonic neuron. IEEE Photonics J, 8, 1-9(2016).

    [33] S Y Xiang, H Zhang, X X Guo et al. Cascadable neuron-like spiking dynamics in coupled VCSELs subject to orthogonally polarized optical pulse injection. IEEE J Sel Top Quantum Electron, 23, 1-7(2017).

    [34] S Y Xiang, Y H Zhang, X X Guo et al. Photonic generation of neuron-like dynamics using VCSELs subject to double polarized optical injection. J Lightwave Technol, 36, 4227-4234(2018).

    [35] Y H Zhang, S Y Xiang, J K Gong et al. Spike encoding and storage properties in mutually coupled vertical-cavity surface-emitting lasers subject to optical pulse injection. Appl Opt, 57, 1731(2018).

    [36] Y H Zhang, S Y Xiang, X X Guo et al. Polarization-resolved and polarization- multiplexed spike encoding properties in photonic neuron based on VCSEL-SA. Sci Rep, 8, 16095(2018).

    [37] Y Zhang, S Xiang, X Guo et al. All-optical inhibitory dynamics in photonic neuron based on polarization mode competition in a VCSEL with an embedded saturable absorber. Opt Lett, 44, 1548-1551(2019).

    [38] S Y Xiang, Z X Ren, Y H Zhang et al. All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on a VCSEL-SA. Opt Lett, 45, 1104-1107(2020).

    [39] S Y Xiang, J K Gong, Y H Zhang et al. Numerical implementation of wavelength-dependent photonic spike timing dependent plasticity based on VCSOA. IEEE J Quantum Electron, 54, 8100107(2018).

    [40] Z W Song, S Y Xiang, X Y Cao et al. Experimental demonstration of photonic spike-timing-dependent plasticity based on a VCSOA. Sci China Inf Sci, 65, 182401(2022).

    [41] S Y Xiang, Y N Han, X X Guo et al. Real-time optical spike-timing dependent plasticity in a single VCSEL with dual-polarized pulsed optical injection. Sci China Inf Sci, 63, 160405(2020).

    [42] S Y Xiang, Y H Zhang, J K Gong et al. STDP-based unsupervised spike pattern learning in a photonic spiking neural network With VCSELs and VCSOAs. IEEE J Sel Top Quantum Electron, 25, 1700109(2019).

    [43] S Y Xiang, Z X Ren, Z W Song et al. Computing primitive of fully VCSEL-based all-optical spiking neural network for supervised learning and pattern classification. IEEE Trans Neural Networks Learn Syst, 32, 2494-2505(2021).

    [44] C T Fu, S Y Xiang, Y N Han et al. Multilayer photonic spiking neural networks: generalized supervised learning algorithm and network optimization. Photonics, 9, 217(2022).

    [45] Y H Zhang, S Y Xiang, X X Guo et al. The winner-take-all mechanism for all-optical systems of pattern recognition and max-pooling operation. J Lightwave Technol, 38, 5071-5077(2020).

    [46] Y N Han, S Y Xiang, Z X Ren et al. Delay-weight plasticity-based supervised learning in optical spiking neural networks. Photonics Res, 9, B119-B127(2021).

    [47] Z W Song, S Y Xiang, Z X Ren et al. Photonic spiking neural network based on excitable VCSELs-SA for sound azimuth detection. Opt Express, 28, 1561-1573(2020).

    [48] Z W Song, S Y Xiang, Z X Ren et al. Spike sequence learning in a photonic spiking neural network consisting of VCSELs-SA with supervised training. IEEE J Sel Top Quantum Electron, 26, 1700209(2020).

    [49] S H Wang, S Y Xiang, G Q Han et al. Photonic associative learning neural network based on VCSELs and STDP. J Lightwave Technol, 38, 4691-4698(2020).

    [50] Y H Zhang, S Y Xiang, X X Guo et al. A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns. Sci China Inf Sci, 64, 122403(2021).

    [51] S Gao, S Y Xiang, Z W Song et al. All-optical Sudoku solver with photonic spiking neural network. Opt Commun, 495, 127068(2021).

    [52] S Gao, S Y Xiang, Z W Song et al. Motion detection and direction recognition in a photonic spiking neural network consisting of VCSELs-SA. Opt Express, 30, 31701-31713(2022).

    [53] S Y Xiang, Z X Ren, Y H Zhang et al. Training a multi-layer photonic spiking neural network with modified supervised learning algorithm based on photonic STDP. IEEE J Sel Top Quantum Electron, 27, 7500109(2021).

    [54] Y H Zhang, S Y Xiang, Y N Han et al. BP-based supervised learning algorithm for multilayer photonic spiking neural network and hardware implementation. Opt Express, 31, 16549-16559(2023).

    [55] Z W Song, S Y Xiang, S H Zhao et al. A multi-layer photonic spiking neural network with a modified backpropagation algorithm for nonlinear classification. Opt Commun, 546, 129806(2023).

    [56] S Y Xiang, T R Zhang, Y N Han et al. Neuromorphic speech recognition with photonic convolutional spiking neural networks. IEEE J Sel Top Quantum Electron, 29, 7600507(2023).

    [57] Y N Han, S Y Xiang, Y N Zhang et al. An all-MRR-based photonic spiking neural network for spike sequence learning. Photonics, 9, 120(2022).

    [58] Y N Zhang, S Y Xiang, Y N Han et al. Supervised learning and pattern recognition in photonic spiking neural networks based on MRR and phase-change materials. Opt Commun, 549, 129870(2023).

    [59] Z W Song, S Y Xiang, S T Zhao et al. A hybrid-integrated photonic spiking neural network framework based on an MZI array and VCSELs-SA. IEEE J Sel Top Quantum Electron, 29, 8300211(2023).

    [60] D Z Zheng, S Y Xiang, X X Guo et al. Experimental demonstration of coherent photonic neural computing based on a Fabry–Perot laser with a saturable absorber. Photonics Res, 11, 65-71(2023).

    [61] Z W Song, S Y Xiang, X X Guo et al. Nonlinear neural computation in an integrated FP-SA spiking neuron subject to incoherent dual-wavelength optical pulse injections. Sci China Inf Sci, 66, 229405(2023).

    [62] S Y Xiang, Y C Shi, X X Guo et al. Hardware-algorithm collaborative computing with photonic spiking neuron chip based on an integrated Fabry–Perot laser with a saturable absorber. Optica, 10, 162-171(2023).

    [63] X X Guo, S Y Xiang, Y H Zhang et al. Hardware implementation of multi-layer photonic spiking neural network with three cascaded photonic spiking neurons. J Lightwave Technol, 41, 6533-6541(2023).

    [64] Y N Han, S Y Xiang, S Gao et al. Experimental demonstration of delay-weight learning and pattern classification with a FP-SA-based photonic spiking neuron chip. J Lightwave Technol, 42, 1497-1503(2024).

    [65] Y H Zhang, S Y Xiang, X X Guo et al. Spiking information processing in a single photonic spiking neuron chip with double integrated electronic dendrites. Photonics Res, 11, 2033-2041(2023).

    [66] S Gao, S Y Xiang, Z W Song et al. Hardware implementation of ultra-fast obstacle avoidance based on a single photonic spiking neuron. Laser Photonics Rev, 17, 2300424(2023).

    [67] S Y Xiang, S Gao, Y C Shi et al. Experimental demonstration of a photonic spiking neuron based on a DFB laser subject to side-mode optical pulse injection. Sci China Inf Sci, 67, 132402(2024).

    [68] S Gao, S Y Xiang, D Z Zheng et al. Cascadable excitability and inhibition in DFB laser-based photonic spiking neurons. Opt Commun, 554, 130207(2024).

    [69] Y N Zhang, S Y Xiang, Z W Song et al. Evolution of neuron-like spiking response and spike-based all-optical XOR operation in a DFB with saturable absorber. J Lightwave Technol, 42, 2026-2035(2024).

    [70] C Y Yu, S Y Xiang, Y N Zhang et al. Neuromorphic convolution with a spiking DFB-SA laser neuron based on rate coding. Opt Express, 31, 43698-43711(2023).

    [71] Y N Han, S Y Xiang, Z W Song et al. Pattern recognition in multi-synaptic photonic spiking neural networks based on a DFB-SA chip. Opto-Electron Sci, 2, 230021-230021(2023).

    [72] S Y Xiang, Y C Shi, Y H Zhang et al. Photonic integrated neuro-synaptic core for convolutional spiking neural network. Opto-Electron Adv, 6, 230140(2023).

    [73] A Hurtado, I D Henning, M J Adams. Optical neuron using polarisation switching in a 1550nm-VCSEL. Opt Express, 18, 25170-25176(2010).

    [74] A Hurtado, K Schires, I D Henning et al. Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems. Appl Phys Lett, 100, 103703(2012).

    [75] J Robertson, T Deng, J Javaloyes et al. Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments. Opt Lett, 42, 1560-1563(2017).

    [76] A Hurtado, J Javaloyes. Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems. Appl Phys Lett, 107, 241103(2015).

    [77] T Deng, J Robertson, A Hurtado. Controlled propagation of spiking dynamics in vertical-cavity surface-emitting lasers: towards neuromorphic photonic networks. IEEE J Sel Top Quantum Electron, 23, 1800408(2017).

    [78] J Robertson, M Hejda, J Bueno et al. Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons. Sci Rep, 10, 6098(2020).

    [79] J Robertson, E Wade, Y Kopp et al. Toward neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE J Sel Top Quantum Electron, 26, 7700715(2020).

    [80] J Robertson, P Kirkland, J A Alanis et al. Ultrafast neuromorphic photonic image processing with a VCSEL neuron. Sci Rep, 12, 4874(2022).

    [81] J Robertson, P Kirkland, Caterina G Di et al. VCSEL-based photonic spiking neural networks for ultrafast detection and tracking. Neuromorph Comput Eng, 4, 014010(2024).

    [82] Z J Chen, A Sludds, R Davis et al. Deep learning with coherent VCSEL neural networks. Nat Photonics, 17, 723-730(2023).

    [83] J W Wang, F Sciarrino, A Laing et al. Integrated photonic quantum technologies. Nat Photonics, 14, 273-284(2020).

    [84] A N Tait, Lima T F De, E Zhou et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci Rep, 7, 7430(2017).

    [86] P Y Ma, A N Tait, Lima T F De et al. Photonic independent component analysis using an on-chip microring weight bank. Opt Express, 28, 1827-1844(2020).

    [87] V Bangari, B A Marquez, H Miller et al. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs). IEEE J Sel Top Quantum Electron, 26, 7701213(2020).

    [89] S Ohno, R Tang, K Toprasertpong et al. Si microring resonator crossbar array for on-chip inference and training of the optical neural network. ACS Photonics, 9, 2614-2622(2022).

    [90] S F Xu, J Wang, S C Yi et al. High-order tensor flow processing using integrated photonic circuits. Nat Commun, 13, 7970(2022).

    [91] B W Bai, Q P Yang, H W Shu et al. Microcomb-based integrated photonic processing unit. Nat Commun, 14, 66(2023).

    [92] M Reck, A Zeilinger, H J Bernstein et al. Experimental realization of any discrete unitary operator. Phys Rev Lett, 73, 58-61(1994).

    [93] W R Clements, P C Humphreys, B J Metcalf et al. Optimal design for universal multiport interferometers. Optica, 3, 1460-1465(2016).

    [94] Y C Shen, N C Harris, S Skirlo et al. Deep learning with coherent nanophotonic circuits. Nat Photonics, 11, 441-446(2017).

    [96] M Y S Fang, S Manipatruni, C Wierzynski et al. Design of optical neural networks with component imprecisions. Opt Express, 27, 14009-14029(2019).

    [97] T Zhang, J Wang, Y H Dan et al. Efficient training and design of photonic neural network through neuroevolution. Opt Express, 27, 37150-37163(2019).

    [98] F Shokraneh, S Geoffroy-gagnon, O Liboiron-Ladouceur. The diamond mesh, a phase-error- and loss-tolerant field-programmable MZI-based optical processor for optical neural networks. Opt Express, 28, 23495-23508(2020).

    [100] Y Tian, Y Zhao, S P Liu et al. Scalable and compact photonic neural chip with low learning-capability-loss. Nanophotonics, 11, 329-344(2022).

    [101] H H Zhu, J Zou, H Zhang et al. Space-efficient optical computing with an integrated chip diffractive neural network. Nat Commun, 13, 1044(2022).

    [102] Y Shi, J Y Ren, G Y Chen et al. Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks. Nat Commun, 13, 6048(2022).

    [103] B Wu, S J Liu, J W Cheng et al. Real-valued optical matrix computing with simplified MZI mesh. Intell Comput, 2, 0047(2023).

    [104] C D Wright, Y W Liu, K I Kohary et al. Arithmetic and biologically-inspired computing using phase-change materials. Adv Mater, 23, 3408-3413(2011).

    [105] D Kuzum, R G D Jeyasingh, B Lee et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett, 12, 2179-2186(2012).

    [106] Z G Cheng, C Ríos, W H P Pernice et al. On-chip photonic synapse. Sci Adv, 3, e1700160(2017).

    [107] I Chakraborty, G Saha, K Roy. Photonic in-memory computing primitive for spiking neural networks using phase-change materials. Phys Rev Appl, 11, 014063(2019).

    [108] J Feldmann, N Youngblood, C D Wright et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 569, 208-214(2019).

    [109] J Feldmann, N Youngblood, M Karpov et al. Parallel convolutional processing using an integrated photonic tensor core. Nature, 589, 52-58(2021).

    [110] W Zhou, B W Dong, N Farmakidis et al. In-memory photonic dot-product engine with electrically programmable weight banks. Nat Commun, 14, 2887(2023).

    [111] K Vandoorne, P Mechet, Vaerenbergh T Van et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat Commun, 5, 3541(2014).

    [112] X Y Xu, M X Tan, B Corcoran et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature, 589, 44-51(2021).

    [113] F Ashtiani, A J Geers, F Aflatouni. An on-chip photonic deep neural network for image classification. Nature, 606, 501-506(2022).

    [114] T Z Fu, Y B Zang, Y Y Huang et al. Photonic machine learning with on-chip diffractive optics. Nat Commun, 14, 70(2023).

    [115] X Y Meng, G J Zhang, N N Shi et al. Compact optical convolution processing unit based on multimode interference. Nat Commun, 14, 3000(2023).

    [116] X Lin, Y Rivenson, N T Yardimci et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

    [117] J L Chang, V Sitzmann, X Dun et al. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci Rep, 8, 12324(2018).

    [118] J Bueno, S Maktoobi, L Froehly et al. Reinforcement learning in a large-scale photonic recurrent neural network. Optica, 5, 756-760(2018).

    [119] L D Lu, L Q Zhu, Q K Zhang et al. Miniaturized diffraction grating design and processing for deep neural network. IEEE Photonics Technol Lett, 31, 1952-1955(2019).

    [120] T Yan, J M Wu, T K Zhou et al. Fourier-space diffractive deep neural network. Phys Rev Lett, 123, 023901(2019).

    [121] H Chen, J N Feng, M W Jiang et al. Diffractive deep neural networks at visible wavelengths. Engineering, 7, 1483-1491(2021).

    [122] T K Zhou, X Lin, J M Wu et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat Photonics, 15, 367-373(2021).

    [123] E Goi, X Chen, Q M Zhang et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci Appl, 10, 40(2021).

    [124] T Fujita, H Sakaguchi, J Zhang et al. Magneto-optical diffractive deep neural network. Opt Express, 30, 36889-36899(2022).

    [125] Z Y Duan, H Chen, X Lin. Optical multi-task learning using multi-wavelength diffractive deep neural networks. Nanophotonics, 12, 893-903(2023).

    [126] Y T Chen, M Nazhamaiti, H Xu et al. All-analog photoelectronic chip for high-speed vision tasks. Nature, 623, 48-57(2023).

    [127] Y Zuo, B H Li, Y J Zhao et al. All-optical neural network with nonlinear activation functions. Optica, 6, 1132-1137(2019).

    [128] R Hamerly, L Bernstein, A Sludds et al. Large-scale optical neural networks based on photoelectric multiplication. Phys Rev X, 9, 021032(2019).

    [129] A Sludds, L Bernstein, R Hamerly et al. A scalable optical neural network architecture using coherent detection. Proc SPIE, 11299, 112990H(2020).

    [130] M Rafayelyan, J Dong, Y Q Tan et al. Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction. Phys Rev X, 10, 041037(2020).

    [131] Z H Xu, T K Zhou, M Z Ma et al. Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence. Science, 384, 202-209(2024).

    [132] C Qian, X Lin, X B Lin et al. Performing optical logic operations by a diffractive neural network. Light Sci Appl, 9, 59(2020).

    [133] C M Wu, H S Yu, S Lee et al. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nat Commun, 12, 96(2021).

    [134] C Liu, Q Ma, Z J Luo et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat Electron, 5, 113-122(2022).

    [136] G Mourgias-Alexandris, M Moralis-Pegios, A Tsakyridis et al. Noise-resilient and high-speed deep learning with coherent silicon photonics. Nat Commun, 13, 5572(2022).

    [137] M Kirtas, A Oikonomou, N Passalis et al. Quantization-aware training for low precision photonic neural networks. Neural Networks, 155, 561-573(2022).

    [138] C H Feng, J Q Gu, H Q Zhu et al. A compact butterfly-style silicon photonic–electronic neural chip for hardware-efficient deep learning. ACS Photonics, 9, 3906-3916(2022).

    [139] Y C Zhan, H Zhang, H X Lin et al. Physics-aware analytic-gradient training of photonic neural networks. Laser Photonics Rev, 18, 2300445(2024).

    [140] T W Hughes, M Minkov, Y Shi et al. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica, 5, 864-871(2018).

    [141] T K Zhou, L Fang, T Yan et al. In situ optical backpropagation training of diffractive optical neural networks. Photonics Res, 8, 940-953(2020).

    [142] Z Y Zheng, Z Y Duan, H Chen et al. Dual adaptive training of photonic neural networks. Nat Mach Intell, 5, 1119-1129(2023).

    [143] T W Wu, M Menarini, Z H Gao et al. Lithography-free reconfigurable integrated photonic processor. Nat Photonics, 17, 710-716(2023).

    [144] S Pai, Z H Sun, T W Hughes et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science, 380, 398-404(2023).

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    Shuiying Xiang, Ziwei Song, Yahui Zhang, Xingxing Guo, Yanan Han, Yue Hao. Progress in the research of optical neural networks[J]. Opto-Electronic Engineering, 2024, 51(7): 240101

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    Received: May. 4, 2024

    Accepted: Jun. 28, 2024

    Published Online: Nov. 12, 2024

    The Author Email: Xiang Shuiying (项水英)

    DOI:10.12086/oee.2024.240101

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