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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[18] Schemmel J, Brüderle D, Grübl A et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling[C], 1947-1950(2010). https://doi.org/10.1109/ISCAS.2010.5536970

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

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

[21] Orchard G, Frady E P, Rubin D B D et al. Efficient neuromorphic signal processing with loihi 2[C], 254-259(2021). https://doi.org/10.1109/SiPS52927.2021.00053

[22] Shi L P, Pei J, Deng N et al. Development of a neuromorphic computing system[C], 4.3.1-4.3.4(2015). https://doi.org/10.1109/IEDM.2015.7409624

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

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

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

[26] Tait A N, Nahmias M A, Tian Y, Naruse M et al. Photonic neuromorphic signal processing and computing[M]. Nanophotonic Information Physics: Nanointelligence and Nanophotonic Computing, 183-222(2014). https://doi.org/10.1007/978-3-642-40224-1_8

[27] Shastri B J, Chang J, Tait A N, Wabnitz S, Eggleton B J et al. Ultrafast optical techniques for communication networks and signal processing[M]. All-Optical Signal Processing: Data Communication and Storage Applications, 469-503(2015). https://doi.org/10.1007/978-3-319-14992-9_15

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[85] Mehrabian A, Al-Kabani Y, Sorger V J et al. PCNNA: a photonic convolutional neural network accelerator[C], 169-173(2018). https://doi.org/10.1109/SOCC.2018.8618542

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

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

[88] Sunny F, Mirza A, Nikdast M et al. CrossLight: a cross-layer optimized silicon photonic neural network accelerator[C], 1069-1074(2021). https://doi.org/10.1109/DAC18074.2021.9586161

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

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

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

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

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

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

[95] George J K, Nejadriahi H, Sorger V J. Towards on-chip optical FFTs for convolutional neural networks[C], 1-4(2017). https://doi.org/10.1109/ICRC.2017.8123675

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

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

[98] 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]. Opt Express, 28, 23495-23508(2020).

[99] Shokraneh F, Geoffroy-Gagnon S, Liboiron-Ladouceur O. Towards phase-error- and loss-tolerant programmable MZI-based optical processors for optical neural networks[C], 1-2(2020). https://doi.org/10.1109/IPC47351.2020.9252466

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[135] Gu J Q, Zhao Z, Feng C H et al. ROQ: a noise-aware quantization scheme towards robust optical neural networks with low-bit controls[C], 1586-1589(2020). https://doi.org/10.23919/DATE48585.2020.9116521

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

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

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

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

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

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

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

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

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