J. Semicond. > 2021, Volume 42 > Issue 2 > 023105
|

REVIEWS

A review: Photonics devices, architectures, and algorithms for optical neural computing

Shuiying Xiang1, 2, , Yanan Han1, Ziwei Song1, Xingxing Guo1, Yahui Zhang1, Zhenxing Ren1, Suhong Wang1, Yuanting Ma1, Weiwen Zou3, Bowen Ma3, Shaofu Xu3, Jianji Dong4, Hailong Zhou4, Quansheng Ren5, Tao Deng6, Yan Liu2, Genquan Han2 and Yue Hao2

+ Author Affiliations

 Corresponding author: Shuiying Xiang, syxiang@xidian.edu.cn

DOI: 10.1088/1674-4926/42/2/023105

PDF

Turn off MathJax

Abstract: The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era. Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed, wide bandwidth, and massive parallelism. Here, we offer a review on the optical neural computing in our research groups at the device and system levels. The photonics neuron and photonics synapse plasticity are presented. In addition, we introduce several optical neural computing architectures and algorithms including photonic spiking neural network, photonic convolutional neural network, photonic matrix computation, photonic reservoir computing, and photonic reinforcement learning. Finally, we summarize the major challenges faced by photonic neuromorphic computing, and propose promising solutions and perspectives.

Key words: photonics neuron, photonic STDP, photonic spiking neural network, optical reservoir computing, optical convolutional neural network, neuromorphic photonics



[1]
Moore G E. Cramming more components onto integrated circuits. Electron, 1965, 38(8), 114
[2]
Waldrop M M. The chips are down for Moore’s law. Nat News, 2016, 530(7589), 144 doi: 10.1038/530144a
[3]
Maass W. Networks of spiking neurons: The third generation of neural network models. Neur Netw, 1997, 10(9), 1659 doi: 10.1016/S0893-6080(97)00011-7
[4]
Mainen Z F, Sejnowski T J. Reliability of spike timing in neocortical neurons. Science, 1995, 268(5216), 1503 doi: 10.1126/science.7770778
[5]
Hopfield J J. Pattern recognition computation using action potential timing for stimulus representation. Nature, 1995, 376(6535), 33 doi: 10.1038/376033a0
[6]
Bi G Q, Poo M M. Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci, 1998, 18(24), 10464 doi: 10.1523/JNEUROSCI.18-24-10464.1998
[7]
Abbott L F, Nelson S B. Synaptic plasticity: Taming the beast. Nat Neurosci, 2000, 3, 1178 doi: 10.1038/81453
[8]
Bi G Q, Poo M M. Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu Rev Neurosci, 2001, 24, 139 doi: 10.1146/annurev.neuro.24.1.139
[9]
Schuman C D, Potok T E, Patton R M, et al. A survey of neuromorphic computing and neural networks in hardware. arXiv preprint arXiv: 1705. 06963, 2017
[10]
Roy K, Jaiswal A, Panda P. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575, 607 doi: 10.1038/s41586-019-1677-2
[11]
Zhu J D, Zhang T, Yang Y C, et al. A comprehensive review on emerging artificial neuromorphic devices. Appl Phys Rev, 2020, 7, 011312 doi: 10.1063/1.5118217
[12]
Zhang W, Gao B, Tang J, et al. Neuro-inspired computing chips. Nat Electron, 2020, 3, 371 doi: 10.1038/s41928-020-0435-7
[13]
Prucnal P R, Shastri B J, de Lima T F, et al. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv Opt Photon, 2016, 8(2), 228 doi: 10.1364/AOP.8.000228
[14]
Nahmias M A, Shastri B J, Tait A N, et al. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE J Sel Top Quantum Electron, 2013, 19(5), 1800212 doi: 10.1109/JSTQE.2013.2257700
[15]
Gholipour B, Bastock P, Craig C, et al. Amorphous metal-sulphide microfibers enable photonic synapses for brain-like computing. Adv Opt Mater, 2015, 5(3), 635 doi: 10.1002/adom.201570029
[16]
Cheng Z, Ríos C, Pernice W H P, et al. On-chip photonic synapse. Sci Adv, 2017, 3(9), e1700160 doi: 10.1126/sciadv.1700160
[17]
Feldmann J, Youngblood, Wright N C D, et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 2019, 569, 208 doi: 10.1038/s41586-019-1157-8
[18]
Zhuge X, Wang J, Zhuge F. Photonic synapses for ultrahigh-speed neuromorphic computing. Phys Status Solidi RRL, 2019, 13, 1900082 doi: 10.1002/pssr.201900082
[19]
de Lima T F, Peng H T, Tait A N, et al. Machine learning with neuromorphic photonics. J Lightwave Technol, 2019, 37(5), 1515 doi: 10.1109/JLT.2019.2903474
[20]
Zou W W, Ma B W, Xu S F, et al. Towards an intelligent photonic system. Sci China Inform Sci, 2020, 63, 160401 doi: 10.1007/s11432-020-2863-y
[21]
Shastri B J, Tait A N, de Lima T F, et al. Photonics for artificial intelligence and neuromorphic computing. arXiv preprint arXiv: 2011.00111v1, 2020
[22]
Hurtado A, Henning I D, Adams M J. Optical neuron using polarization switching in a 1550 nm-VCSEL. Opt Express, 2010, 18(24), 25170 doi: 10.1364/OE.18.025170
[23]
Coomans W, Gelens L, Beri S, et al. Solitary and coupled semiconductor ring lasers as optical spiking neurons. Phys Rev E, 2011, 84(3), 036209 doi: 10.1103/PhysRevE.84.036209
[24]
Hurtado A, Schires K, Henning I, et al. Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems. Appl Phys Lett, 2012, 100(10), 103703 doi: 10.1063/1.3692726
[25]
Xiang S Y, Wen A J, Pan W. Emulation of spiking response and spiking frequency property in VCSEL-based photonic neuron. IEEE Photonics J, 2016, 8(5), 1504109 doi: 10.1109/JPHOT.2016.2614104
[26]
Robertson J, Deng T, Javaloyes J. Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments. Opt Lett, 2017, 42(8), 1560 doi: 10.1364/OL.42.001560
[27]
Xiang S Y, Zhang Y H, Guo X X, et al. Cascadable neuron-like spiking dynamics in coupled VCSELs subject to orthogonally polarized optical pulse injection. IEEE J Sel Top Quantum Electron, 2017, 23(6), 1700207 doi: 10.1109/JSTQE.2017.2678170
[28]
Deng T, Robertson J, Hurtado A. Controlled propagation of spiking dynamics in vertical-cavity surface-emitting lasers: towards neuromorphic photonic networks. IEEE J Sel Top Quantum Electron, 2017, 23(6), 1800408 doi: 10.1109/JSTQE.2017.2685140
[29]
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 Lightwave Technol, 2018, 36(19), 4227 doi: 10.1109/JLT.2018.2818195
[30]
Deng T, Robertson J, Wu Z M, et al. Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks. IEEE Access, 2018, 6, 67951 doi: 10.1109/ACCESS.2018.2878940
[31]
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. Sci Rep, 2018, 8, 16095 doi: 10.1038/s41598-018-34537-x
[32]
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. Appl Opt, 2018, 57(7), 1731 doi: 10.1364/AO.57.001731
[33]
Zhang Y H, Xiang S Y, Guo X X, et al. All-optical inhibitory dynamics in photonic neuron based on polarization mode competition in a VCSEL with an embedded saturable absorber. Opt Lett, 2019, 44(7), 1548 doi: 10.1364/OL.44.001548
[34]
Xiang S Y, Ren Z, Zhang Y, et al. All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on VCSEL-SA. Opt Lett, 2020, 45(5), 1104 doi: 10.1364/OL.383942
[35]
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. IEEE J Sel Top Quantum Electron, 2019, 25(6), 1700109 doi: 10.1109/JSTQE.2019.2911565
[36]
Robertson J, Wade, Kopp E Y, et al. Toward neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE J Sel Top Quantum Electron, 2020, 26(1), 7700715 doi: 10.1109/JSTQE.2019.2931215
[37]
Ma B W, Zou W W. Demonstration of a distributed feedback laser diode working as a graded-potential-signaling photonic neuron and its application to neuromorphic information processing. Sci China Inform Sci, 2020, 63, 160408 doi: 10.1007/s11432-020-2887-6
[38]
Ma B W, Chen J P, Zou W W. A DFB-LD-based photonic neuromorphic network for spatiotemporal pattern recognition. Proceedings of Optical Fiber Communication Conference, 2020, M2K.2
[39]
Xiang S Y, Ren Z X, Song Z W, et al. Computing primitive of fully-VCSELs-based all-optical spiking neural network for supervised learning and pattern classification. IEEE Trans Neural Netw Learn Syst, 2020, in press
[40]
Toole R, Fok M P. Photonic implementation of a neuronal algorithm applicable towards angle of arrival detection and localization. Opt Express, 2015, 23(12), 16133 doi: 10.1364/OE.23.016133
[41]
Ren Q S, Zhang Y L, Wang R, et al. Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation. Opt Express, 2015, 23(19), 25247 doi: 10.1364/OE.23.025247
[42]
Toole R, Tait A N, de Lima T F, et al. Photonic implementation of spike-timing-dependent plasticity and learning algorithms of biological neural systems. J Lightwave Technol, 2016, 34(2), 470 doi: 10.1109/JLT.2015.2475275
[43]
Li Q, Wang Z, Le Y S, et al. Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier. Proc SPIE, 2016, 10019, 2245976
[44]
Xiang S Y, Gong J K, Zhang Y H, et al. Numerical implementation of wavelength-dependent photonic spike timing dependent plasticity based on VCSOA. IEEE J Quantum Electron, 2018, 54(6), 8100107 doi: 10.1109/JQE.2018.2879484
[45]
Lima T, Shastri B J, Tait A N, et al. Progress in neuromorphic photonics. Nanophotonics, 2017, 6(3), 577 doi: 10.1515/nanoph-2016-0139
[46]
Song S, Kim J, Kwon S M, et al. Recent progress of optoelectronic and all-optical neuromorphic devices: a comprehensive review of device structures, materials, and applications. Adv Intell Syst, 2020, 2000119 doi: 10.1002/aisy.202000119
[47]
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. Sci China Inform Sci, 2020, 63, 160405 doi: 10.1007/s11432-020-2820-y
[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. IEEE J Sel Top Quantum Electron, 2020, 26(5), 1700209 doi: 10.1109/JSTQE.2020.2975564
[49]
Song Z W, Xiang S Y, Ren Z X, et al. Photonic spiking neural network based on excitable VCSELs-SA for sound azimuth detection. Opt Express, 2020, 28(2), 1561 doi: 10.1364/OE.381229
[50]
Zhang Y H, Xiang S Y, Guo X X, A. Wen, et al The winner-take-all mechanism for all-optical systems of pattern recognition and max-pooling operation. J Lightwave Technol, 2020, 38(18), 5071 doi: 10.1109/JLT.2020.3000670
[51]
Wang S H, Xiang S Y, Han G Q, et al. Photonic associative learning neural network based on VCSELs and STDP. J Lightwave Technol, 2020, 38(17), 4691 doi: 10.1109/JLT.2020.2995083
[52]
Xu S F, Wang J, Wang R, et al. High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays. Opt Express, 2019, 27, 19778 doi: 10.1364/OE.27.019778
[53]
Xu S F, Wang J, Zou W W. Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines. Opt Lett, 2020, 45, 3689 doi: 10.1364/OL.397344
[54]
Xu S F, Zou X T, Ma B W, et al. Deep-learning-powered photonic analog-to digital conversion. Light Sci Appl, 2019, 8(1), 66 doi: 10.1038/s41377-019-0176-4
[55]
Zhou H L, Zhao Y H, Xu G X, et al. Chip-scale optical matrix computation for PageRank algorithm. IEEE J Sel Top Quantum Electron, 2020, 26, 8300910 doi: 10.1109/JSTQE.2019.2943347
[56]
Zhao Y H, Zhou H L, Dong J J. An optical processor for matrix computation on silicon-on-insulator. International Conference on Photonics in Switching and Computing OptoElectronics and Communications Conference, 2019
[57]
Zhou H L, Zhao Y H, Wei Y X, et al. All-in-one silicon photonic polarization processor. Nanophotonics, 2019, 8, 2257 doi: 10.1515/nanoph-2019-0310
[58]
Zhou H L, Zhao Y H, Wei Y X, et al. Multipurpose photonic polarization processor chip. Asia Communications and Photonics Conference, 2019, M4A.229
[59]
Zhou H L, Zhao Y H, Wang X, et al. Self-configuring and reconfigurable silicon photonic signal processor. ACS Photonics, 2020, 7, 792 doi: 10.1021/acsphotonics.9b01673
[60]
Maass W, Natschlager T, Markram H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neur Comput, 2002, 14(11), 2531 doi: 10.1162/089976602760407955
[61]
Maass W, Natschlager T, Markram H. Fading memory and kernel properties of generic cortical microcircuit models. J Physiol-Paris, 2004, 98(4–6), 315 doi: 10.1016/j.jphysparis.2005.09.020
[62]
Lukosevicius M, Jaeger H. Reservoir computing approaches to recurrent neural network training. Comput Sci Rev, 2009, 3(3), 127 doi: 10.1016/j.cosrev.2009.03.005
[63]
Guy V D S, Brunner D, Soriano M C. Advances in photonic reservoir computing. Nanophotonics, 2017, 6(3), 561 doi: 10.1515/nanoph-2016-0132
[64]
Brunner D, Penkovsky B, Marquez B A, et al. Tutorial: Photonic neural networks in delay systems. J Appl Phys, 2018, 124(15), 152004 doi: 10.1063/1.5042342
[65]
Tanaka G, Yamane T, Héroux J B, et al. Recent advances in physical reservoir computing: A review. Neur Netw, 2019, 115, 100 doi: 10.1016/j.neunet.2019.03.005
[66]
Guo X X, Xiang S Y, Zhang Y H, et al. Polarization multiplexing reservoir computing based on a VCSEL with polarized optical feedback. IEEE J Sel Top Quantum Electron, 2020, 26(1), 1700109 doi: 10.1109/JSTQE.2019.2932023
[67]
Guo X X, Xiang S Y, Zhang Y H, et al. Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system. Opt Express, 2019, 27(16), 23293 doi: 10.1364/OE.27.023293
[68]
Guo X X, Xiang S Y, Zhang Y H, et al. Enhanced memory capacity of a neuromorphic reservoir computing system based on a VCSEL with double optical feedbacks. Sci China Inf Sci, 2020, 63(6), 160407 doi: 10.1007/s11432-020-2862-7
[69]
Guo X X, Xiang S Y, Zhang Y H, et al. High-speed neuromorphic reservoir computing based on a semiconductor nanolaser with optical feedback under electrical modulation. IEEE J Sel Top Quantum Electron, 2020, 26(5), 1500707 doi: 10.1109/JSTQE.2020.2987077
[70]
Guo X X, Xiang S Y, Y. Qu, et al Enhanced prediction performance of a neuromorphic reservoir computing using a semiconductor nanolaser with double phase conjugate feedbacks. J Lightwave Technol, 2021, 39(1), 129 doi: 10.1109/JLT.2020.3023451
[71]
Sutton R S, Barto A G. Reinforcement learning: an introduction. The MIT Press Cambridge, Massachusetts London, England, 1998, 712192
[72]
Naruse M, Mihana T, Hori H, et al. Scalable photonic reinforcement learning by time-division multiplexing of laser chaos. Sci Rep, 2018, 8(1), 10890 doi: 10.1038/s41598-018-29117-y
[73]
Ma Y T, Xiang S Y, Guo X X, et al. Time-delay signature concealment of chaos and ultrafast decision making in mutually coupled semiconductor lasers with a phase-modulated Sagnac loop. Opt Express, 2020, 28, 1665 doi: 10.1364/OE.384378
[74]
Han Y N, Xiang S Y, Wang Y, et al. Generation of multi-channel chaotic signals with time delay signature concealment and ultrafast photonic decision making based on globally-coupled semiconductor lasers network. Photonics Res, 2020, 8(11), 1792 doi: 10.1364/PRJ.403319
[75]
Zhou Z, Tu Z, Yin B, et al. Development trends in silicon photonics. Chin Opt Lett, 2013, 11(1), 012501 doi: 10.3788/COL201311.012501
[76]
Zhou Z P, Yin B, Michel J. On-chip light sources for silicon photonics. Light Sci Appl, 2015, 4, e358 doi: 10.1038/lsa.2015.131
[77]
Atabaki A H, Moazeni S, Pavanello F, et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature, 2018, 556, 349 doi: 10.1038/s41586-018-0028-z
[78]
Billah M R, Blaicher M, Hoose T, et al. Hybrid integration of silicon photonics circuits and InP lasers by photonic wire bonding. Optica, 2018, 5, 876 doi: 10.1364/OPTICA.5.000876
[79]
Guo X H, He A, Su Y K. Recent advances of heterogeneously integrated IIIV laser on Si. J Semicond, 2019, 40(10), 101304 doi: 10.1088/1674-4926/40/10/101304
[80]
Bai B W, Shu H W, Wang X J, et al. Towards silicon photonic neural networks for artificial intelligence. Sci China Inf Sci, 2020, 63(6), 160403 doi: 10.1007/s11432-020-2872-3
[81]
Bao S Y, Wang Y, Lina K, et al. A review of silicon-based wafer bonding processes, an approach to realize the monolithic integration of Si-CMOS and IIIV-on-Si wafers. J Semicond, 2020, in press
[82]
Ruan Z L, Zhu Y T, Chen P X, et al. Efficient hybrid integration of long-wavelength VCSELs on silicon photonic circuits. J Lightwave Technol, 2020, 38(18), 5100 doi: 10.1109/JLT.2020.2999526
[83]
Li Y Y, Wang Y, Yang D R, et al. Recent progress on optoelectronic synaptic devices. Sci Sin Inform, 2020, 50, 892 doi: 10.1360/SSI-2019-0248
[84]
Wetzstein G, Ozcan A, Gigan S, et al. Inference in artificial intelligence with deep optics and photonics. Nature, 2020, 588, 39 doi: 10.1038/s41586-020-2973-6
Fig. 1.  (Color online) (a) Temporal output of the spike encoding based on the modeling-based photonic neuron. (b) Threshold-like response and (c) spike latency property of the modeling-based photonic neuron. © [2020] IEEE. Reprinted with permission from Ref. [39].

DownLoad: Full-Size Img PowerPoint
Fig. 2.  (a) Schematic diagram of inhibitory neuron based on VSCEL-SA. Reprinted from Ref. [31]. (b) The results of inhibition in a photonic neuron. Reprinted with permission from Ref. [33]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 3.  (Color online) (a) Schematic diagram of all-optical exclusive OR (XOR) operator based on a single VCSEL-SA. (b) XOR output for different sets of inputs. (c) The inputs and outputs of XOR for two RZ sequences. Reprinted with permission from Ref. [34]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 4.  (Color online) (a) The experimental setup for spiking firing and inhibition of VCSEL-based neuron. © [2018] IEEE. Reprinted with permission from Ref. [30]. (b) Time series of fired spiking responses. Reprinted with permission from Ref. [26]. © The Optical Society. © [2017] IEEE. Reprinted with permission from Ref. [28]. (c) Time series of suppressed spiking response. © [2018] IEEE. Reprinted with permission from Ref. [30].

DownLoad: Full-Size Img PowerPoint
Fig. 5.  (Color online) (a) The experimental setup for graded-potential-signaling-based neuromorphic processing applications with the optical neuron based on DFB, including (b, c) pattern recognition, (d, e) single-wavelength implementation of STDP, and (f, g) sound azimuth measurement[37]. Reproduced with permission. © 2020 Springer Nature.

DownLoad: Full-Size Img PowerPoint
Fig. 6.  (Color online) (a) The DFB-based spatiotemporal pattern recognition network with STDP learning module. The network output for patterns with (b) 3 and (c) 4 input branches, respectively. Reprinted with permission from Ref. [38]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 7.  (Color online) (a) Optical implementation of STDP. (b) The measured learning window of Optical STDP with different SOA driving current. Reprinted with permission from Ref. [41]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 8.  (Color online) (a) Schematic diagram of photonic STDP based on VCSOA. (b) The experimental measured output pulse train corresponding to the input pulse pairs with different time interval. (c) Simulated input pulse. (d) Simulated output pulse. (e) The calculated STDP curve. Pulse 1 (Pulse 2): the optical pulse injection beam; VODL: variable optical delay line, OC: optical coupler; Circulator: optical circulator; VCSOA: vertical-cavity semiconductor optical amplifier. Bias and TEC: The bias current and temperature controller for VCSOA; $ {\lambda _{1,2}}$ in the box means a bandpass filter. © [2018] IEEE. Reprinted with permission from Ref. [44].

DownLoad: Full-Size Img PowerPoint
Fig. 9.  (Color online) (a) Schematic diagram of photonic SNN based on VCSELs and VCSOAs. (b) PST as a function of the learning cycle. (c) Synaptic weights evolution during the learing process. n photonic presynaptic neurons and one postsynaptic neuron are conneted with optical STDP synapses. VCSEL1–VCSELn: photonic presynaptic neurons; VCSELn+1: photonic postsynaptic neuron; T: variable delay line; Wi (i = 1, 2, …, n): variable synaptic weight device connecting VCSELi and VCSELn+1; STDP array: optical STDP synapses realized by VCSOAs; C: optical coupler. The red dashed box represents the ex-situ approach for updating the synaptic weight. © [2019] IEEE. Reprinted with permission from Ref. [35].

DownLoad: Full-Size Img PowerPoint
Fig. 10.  (Color online) (a) Schematic diagram of a photonic SNN based on VCSEL-SAs for the supervised spike sequence learning. (b-e) Illustration of the spike sequence learning of a typical run. © [2020] IEEE. Reprinted with permission from Ref. [48].

DownLoad: Full-Size Img PowerPoint
Fig. 11.  (Color online) (a) Architecture of the proposed all-optical SNN. (b) An example of a pattern classification task. The network is trained with (b1) a clean character image, and then, the inference was tested with a set of (b2) noisy patterns. (c) Comparison of convergence performance for supervised learning with different I of VCSOA. (d) Accuracy rate of the trained network as a function of the noise strength of the optical digital character. © [2020] IEEE. Reprinted with permission from Ref. [39].

DownLoad: Full-Size Img PowerPoint
Fig. 12.  (a) Schematic structure of a 2 × 2 photonic SNN architecture to detect the sound azimuth, and two PREs correspond to the right ear and left ear, respectively. (b) Responses of POST1 and POST2 when $\Delta {t_i} < 0$ . (c) The calculated $\Delta {t_o}$ as a function of the $\Delta {t_i}$ for different weights ( ${\omega _{12}}$ ). Reprinted with permission from Ref. [49]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 13.  (Color online) (a) Schematic diagram of WTA based on VCSELs-SA. (b) The output of VCSELM,A,B-SA for WTA mechanism. (c) Schematic diagram of pattern recognition based on the WTA machine. (d) The inputs and results of pattern recognition. (e) Schematic diagram of max-pooling operation. (f) The results of max-pooling operation. © [2020] IEEE. Reprinted with permission from Ref. [50].

DownLoad: Full-Size Img PowerPoint
Fig. 14.  (Color online) (a) Schematic diagram of associative learning and forgetting processes based on VCSELs and STDP. (b) The emulation of associative learning and forgetting processes. (c) Schematic diagram of pattern recall. (d) Complete and incomplete patterns of number 8 and 5 respectively, visualization initial and final outputs of number 8 and 5 respectively. (e) The change processes of synaptic weight for number 8 and number 5. © [2020] IEEE. Reprinted with permission from Ref. [51].

DownLoad: Full-Size Img PowerPoint
Fig. 15.  (Color online) (a) The architecture of the optical convolution unit (OCU) by modulator arrays. (b) The transmission rate versus the modulation voltage of the single modulator. (c) An illustration of the serialization method. (d) The convolution results of MNIST-handwritten numbers and Fashion-MNIST data sets. Reprinted with permission from Ref. [52]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 16.  (Color online) (a) The conceptual layout of the optical patching scheme with optical delay lines and wavelength-division-demultiplexing (WDM). (b) The experimental setup of the proposed scheme. Delayed copies of the input waveforms corresponding to (c, d) digit 2 and (e, f) 4, respectively. Reprinted with permission from Ref. [53]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 17.  (Color online) Optical matrix computation and the application for polarization processing. (a) Special-purpose processors for optical matrix computing and polarization processing respectively. (b) Self-configuring example for the smart processors. © [2020] IEEE. Reprinted with permission from Ref. [55].

DownLoad: Full-Size Img PowerPoint
Fig. 18.  (Color online) Experimental results for photonic polarization processor chip. (a) Polarization MIMO descrambler. (b) Polarization controller. (c) Polarization analyzer. Reprinted with permission from Ref. [58]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 19.  (Color online) Experimental results for self-configuring optical signal processor. (a) Multichannel optical switching. (b) Optical MIMO descramble. (c) Tunable optical filter. Reprinted with permission from Ref. [59]. © 2017 American Chemical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 20.  (Color online) (a) System design of the Four-channels RC based on MDC-VCSELs. (b) The virtual node states matrix for each channel. (c) The NMSE values of Four-channels RC system based on MDC-VCSELs as a function of bias current for Four-channels RC and One-channel RC, respectively. Reprinted with permission from Ref. [67]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 21.  (Color online) (a) The conceptual scheme of RC based on a semiconductor nanolaser (SNL) with delayed feedback. (b) The NMSE values of SNL-based RC system as a function of the kd for different F and $\beta $ . © [2020] IEEE. Reprinted with permission from Ref. [69].

DownLoad: Full-Size Img PowerPoint
Fig. 22.  (Color online) (a) Experimental setup of a dual-channels chaotic system with a phase-modulated Sagnac loop. (b) Architecture for reinforcement learning based on dual-channels laser chaos. (c) The CDR as a function of cycle for the CSL-MC system and for the PMSL-MC system. (d) The convergence cycle (CC), at which the CDR reaches 0.9, as a function of coupling strength for the CSL-MC system and the PMSL-MC system. (e) The CDR as a function of the number of cycles for dual-channels and one-channel in the PMSL-MC system. (f) The CC as a function of coupling strength for dual-channels and one-channel in the PMSL-MC system. Reprinted with permission from Ref. [73]. © The Optical Society.

DownLoad: Full-Size Img PowerPoint
Fig. 23.  (Color online) (a) The experimental setup of three globally coupled DFB lasers. (b) A parallel architecture for photonic decision making of 8-armed bandit problem. (c, d) CC and delay concealment as a function of attenuation. (e) The adaptability of the strategy to dynamically changing environment. (f) The scalability to 16-armed problem. Reprinted from Ref. [74]. Copyright (2020) with permission from Chinese Laser Press.

DownLoad: Full-Size Img PowerPoint
[1]
Moore G E. Cramming more components onto integrated circuits. Electron, 1965, 38(8), 114
[2]
Waldrop M M. The chips are down for Moore’s law. Nat News, 2016, 530(7589), 144 doi: 10.1038/530144a
[3]
Maass W. Networks of spiking neurons: The third generation of neural network models. Neur Netw, 1997, 10(9), 1659 doi: 10.1016/S0893-6080(97)00011-7
[4]
Mainen Z F, Sejnowski T J. Reliability of spike timing in neocortical neurons. Science, 1995, 268(5216), 1503 doi: 10.1126/science.7770778
[5]
Hopfield J J. Pattern recognition computation using action potential timing for stimulus representation. Nature, 1995, 376(6535), 33 doi: 10.1038/376033a0
[6]
Bi G Q, Poo M M. Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci, 1998, 18(24), 10464 doi: 10.1523/JNEUROSCI.18-24-10464.1998
[7]
Abbott L F, Nelson S B. Synaptic plasticity: Taming the beast. Nat Neurosci, 2000, 3, 1178 doi: 10.1038/81453
[8]
Bi G Q, Poo M M. Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu Rev Neurosci, 2001, 24, 139 doi: 10.1146/annurev.neuro.24.1.139
[9]
Schuman C D, Potok T E, Patton R M, et al. A survey of neuromorphic computing and neural networks in hardware. arXiv preprint arXiv: 1705. 06963, 2017
[10]
Roy K, Jaiswal A, Panda P. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575, 607 doi: 10.1038/s41586-019-1677-2
[11]
Zhu J D, Zhang T, Yang Y C, et al. A comprehensive review on emerging artificial neuromorphic devices. Appl Phys Rev, 2020, 7, 011312 doi: 10.1063/1.5118217
[12]
Zhang W, Gao B, Tang J, et al. Neuro-inspired computing chips. Nat Electron, 2020, 3, 371 doi: 10.1038/s41928-020-0435-7
[13]
Prucnal P R, Shastri B J, de Lima T F, et al. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv Opt Photon, 2016, 8(2), 228 doi: 10.1364/AOP.8.000228
[14]
Nahmias M A, Shastri B J, Tait A N, et al. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE J Sel Top Quantum Electron, 2013, 19(5), 1800212 doi: 10.1109/JSTQE.2013.2257700
[15]
Gholipour B, Bastock P, Craig C, et al. Amorphous metal-sulphide microfibers enable photonic synapses for brain-like computing. Adv Opt Mater, 2015, 5(3), 635 doi: 10.1002/adom.201570029
[16]
Cheng Z, Ríos C, Pernice W H P, et al. On-chip photonic synapse. Sci Adv, 2017, 3(9), e1700160 doi: 10.1126/sciadv.1700160
[17]
Feldmann J, Youngblood, Wright N C D, et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 2019, 569, 208 doi: 10.1038/s41586-019-1157-8
[18]
Zhuge X, Wang J, Zhuge F. Photonic synapses for ultrahigh-speed neuromorphic computing. Phys Status Solidi RRL, 2019, 13, 1900082 doi: 10.1002/pssr.201900082
[19]
de Lima T F, Peng H T, Tait A N, et al. Machine learning with neuromorphic photonics. J Lightwave Technol, 2019, 37(5), 1515 doi: 10.1109/JLT.2019.2903474
[20]
Zou W W, Ma B W, Xu S F, et al. Towards an intelligent photonic system. Sci China Inform Sci, 2020, 63, 160401 doi: 10.1007/s11432-020-2863-y
[21]
Shastri B J, Tait A N, de Lima T F, et al. Photonics for artificial intelligence and neuromorphic computing. arXiv preprint arXiv: 2011.00111v1, 2020
[22]
Hurtado A, Henning I D, Adams M J. Optical neuron using polarization switching in a 1550 nm-VCSEL. Opt Express, 2010, 18(24), 25170 doi: 10.1364/OE.18.025170
[23]
Coomans W, Gelens L, Beri S, et al. Solitary and coupled semiconductor ring lasers as optical spiking neurons. Phys Rev E, 2011, 84(3), 036209 doi: 10.1103/PhysRevE.84.036209
[24]
Hurtado A, Schires K, Henning I, et al. Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems. Appl Phys Lett, 2012, 100(10), 103703 doi: 10.1063/1.3692726
[25]
Xiang S Y, Wen A J, Pan W. Emulation of spiking response and spiking frequency property in VCSEL-based photonic neuron. IEEE Photonics J, 2016, 8(5), 1504109 doi: 10.1109/JPHOT.2016.2614104
[26]
Robertson J, Deng T, Javaloyes J. Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments. Opt Lett, 2017, 42(8), 1560 doi: 10.1364/OL.42.001560
[27]
Xiang S Y, Zhang Y H, Guo X X, et al. Cascadable neuron-like spiking dynamics in coupled VCSELs subject to orthogonally polarized optical pulse injection. IEEE J Sel Top Quantum Electron, 2017, 23(6), 1700207 doi: 10.1109/JSTQE.2017.2678170
[28]
Deng T, Robertson J, Hurtado A. Controlled propagation of spiking dynamics in vertical-cavity surface-emitting lasers: towards neuromorphic photonic networks. IEEE J Sel Top Quantum Electron, 2017, 23(6), 1800408 doi: 10.1109/JSTQE.2017.2685140
[29]
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 Lightwave Technol, 2018, 36(19), 4227 doi: 10.1109/JLT.2018.2818195
[30]
Deng T, Robertson J, Wu Z M, et al. Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks. IEEE Access, 2018, 6, 67951 doi: 10.1109/ACCESS.2018.2878940
[31]
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. Sci Rep, 2018, 8, 16095 doi: 10.1038/s41598-018-34537-x
[32]
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. Appl Opt, 2018, 57(7), 1731 doi: 10.1364/AO.57.001731
[33]
Zhang Y H, Xiang S Y, Guo X X, et al. All-optical inhibitory dynamics in photonic neuron based on polarization mode competition in a VCSEL with an embedded saturable absorber. Opt Lett, 2019, 44(7), 1548 doi: 10.1364/OL.44.001548
[34]
Xiang S Y, Ren Z, Zhang Y, et al. All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on VCSEL-SA. Opt Lett, 2020, 45(5), 1104 doi: 10.1364/OL.383942
[35]
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. IEEE J Sel Top Quantum Electron, 2019, 25(6), 1700109 doi: 10.1109/JSTQE.2019.2911565
[36]
Robertson J, Wade, Kopp E Y, et al. Toward neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE J Sel Top Quantum Electron, 2020, 26(1), 7700715 doi: 10.1109/JSTQE.2019.2931215
[37]
Ma B W, Zou W W. Demonstration of a distributed feedback laser diode working as a graded-potential-signaling photonic neuron and its application to neuromorphic information processing. Sci China Inform Sci, 2020, 63, 160408 doi: 10.1007/s11432-020-2887-6
[38]
Ma B W, Chen J P, Zou W W. A DFB-LD-based photonic neuromorphic network for spatiotemporal pattern recognition. Proceedings of Optical Fiber Communication Conference, 2020, M2K.2
[39]
Xiang S Y, Ren Z X, Song Z W, et al. Computing primitive of fully-VCSELs-based all-optical spiking neural network for supervised learning and pattern classification. IEEE Trans Neural Netw Learn Syst, 2020, in press
[40]
Toole R, Fok M P. Photonic implementation of a neuronal algorithm applicable towards angle of arrival detection and localization. Opt Express, 2015, 23(12), 16133 doi: 10.1364/OE.23.016133
[41]
Ren Q S, Zhang Y L, Wang R, et al. Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation. Opt Express, 2015, 23(19), 25247 doi: 10.1364/OE.23.025247
[42]
Toole R, Tait A N, de Lima T F, et al. Photonic implementation of spike-timing-dependent plasticity and learning algorithms of biological neural systems. J Lightwave Technol, 2016, 34(2), 470 doi: 10.1109/JLT.2015.2475275
[43]
Li Q, Wang Z, Le Y S, et al. Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier. Proc SPIE, 2016, 10019, 2245976
[44]
Xiang S Y, Gong J K, Zhang Y H, et al. Numerical implementation of wavelength-dependent photonic spike timing dependent plasticity based on VCSOA. IEEE J Quantum Electron, 2018, 54(6), 8100107 doi: 10.1109/JQE.2018.2879484
[45]
Lima T, Shastri B J, Tait A N, et al. Progress in neuromorphic photonics. Nanophotonics, 2017, 6(3), 577 doi: 10.1515/nanoph-2016-0139
[46]
Song S, Kim J, Kwon S M, et al. Recent progress of optoelectronic and all-optical neuromorphic devices: a comprehensive review of device structures, materials, and applications. Adv Intell Syst, 2020, 2000119 doi: 10.1002/aisy.202000119
[47]
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. Sci China Inform Sci, 2020, 63, 160405 doi: 10.1007/s11432-020-2820-y
[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. IEEE J Sel Top Quantum Electron, 2020, 26(5), 1700209 doi: 10.1109/JSTQE.2020.2975564
[49]
Song Z W, Xiang S Y, Ren Z X, et al. Photonic spiking neural network based on excitable VCSELs-SA for sound azimuth detection. Opt Express, 2020, 28(2), 1561 doi: 10.1364/OE.381229
[50]
Zhang Y H, Xiang S Y, Guo X X, A. Wen, et al The winner-take-all mechanism for all-optical systems of pattern recognition and max-pooling operation. J Lightwave Technol, 2020, 38(18), 5071 doi: 10.1109/JLT.2020.3000670
[51]
Wang S H, Xiang S Y, Han G Q, et al. Photonic associative learning neural network based on VCSELs and STDP. J Lightwave Technol, 2020, 38(17), 4691 doi: 10.1109/JLT.2020.2995083
[52]
Xu S F, Wang J, Wang R, et al. High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays. Opt Express, 2019, 27, 19778 doi: 10.1364/OE.27.019778
[53]
Xu S F, Wang J, Zou W W. Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines. Opt Lett, 2020, 45, 3689 doi: 10.1364/OL.397344
[54]
Xu S F, Zou X T, Ma B W, et al. Deep-learning-powered photonic analog-to digital conversion. Light Sci Appl, 2019, 8(1), 66 doi: 10.1038/s41377-019-0176-4
[55]
Zhou H L, Zhao Y H, Xu G X, et al. Chip-scale optical matrix computation for PageRank algorithm. IEEE J Sel Top Quantum Electron, 2020, 26, 8300910 doi: 10.1109/JSTQE.2019.2943347
[56]
Zhao Y H, Zhou H L, Dong J J. An optical processor for matrix computation on silicon-on-insulator. International Conference on Photonics in Switching and Computing OptoElectronics and Communications Conference, 2019
[57]
Zhou H L, Zhao Y H, Wei Y X, et al. All-in-one silicon photonic polarization processor. Nanophotonics, 2019, 8, 2257 doi: 10.1515/nanoph-2019-0310
[58]
Zhou H L, Zhao Y H, Wei Y X, et al. Multipurpose photonic polarization processor chip. Asia Communications and Photonics Conference, 2019, M4A.229
[59]
Zhou H L, Zhao Y H, Wang X, et al. Self-configuring and reconfigurable silicon photonic signal processor. ACS Photonics, 2020, 7, 792 doi: 10.1021/acsphotonics.9b01673
[60]
Maass W, Natschlager T, Markram H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neur Comput, 2002, 14(11), 2531 doi: 10.1162/089976602760407955
[61]
Maass W, Natschlager T, Markram H. Fading memory and kernel properties of generic cortical microcircuit models. J Physiol-Paris, 2004, 98(4–6), 315 doi: 10.1016/j.jphysparis.2005.09.020
[62]
Lukosevicius M, Jaeger H. Reservoir computing approaches to recurrent neural network training. Comput Sci Rev, 2009, 3(3), 127 doi: 10.1016/j.cosrev.2009.03.005
[63]
Guy V D S, Brunner D, Soriano M C. Advances in photonic reservoir computing. Nanophotonics, 2017, 6(3), 561 doi: 10.1515/nanoph-2016-0132
[64]
Brunner D, Penkovsky B, Marquez B A, et al. Tutorial: Photonic neural networks in delay systems. J Appl Phys, 2018, 124(15), 152004 doi: 10.1063/1.5042342
[65]
Tanaka G, Yamane T, Héroux J B, et al. Recent advances in physical reservoir computing: A review. Neur Netw, 2019, 115, 100 doi: 10.1016/j.neunet.2019.03.005
[66]
Guo X X, Xiang S Y, Zhang Y H, et al. Polarization multiplexing reservoir computing based on a VCSEL with polarized optical feedback. IEEE J Sel Top Quantum Electron, 2020, 26(1), 1700109 doi: 10.1109/JSTQE.2019.2932023
[67]
Guo X X, Xiang S Y, Zhang Y H, et al. Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system. Opt Express, 2019, 27(16), 23293 doi: 10.1364/OE.27.023293
[68]
Guo X X, Xiang S Y, Zhang Y H, et al. Enhanced memory capacity of a neuromorphic reservoir computing system based on a VCSEL with double optical feedbacks. Sci China Inf Sci, 2020, 63(6), 160407 doi: 10.1007/s11432-020-2862-7
[69]
Guo X X, Xiang S Y, Zhang Y H, et al. High-speed neuromorphic reservoir computing based on a semiconductor nanolaser with optical feedback under electrical modulation. IEEE J Sel Top Quantum Electron, 2020, 26(5), 1500707 doi: 10.1109/JSTQE.2020.2987077
[70]
Guo X X, Xiang S Y, Y. Qu, et al Enhanced prediction performance of a neuromorphic reservoir computing using a semiconductor nanolaser with double phase conjugate feedbacks. J Lightwave Technol, 2021, 39(1), 129 doi: 10.1109/JLT.2020.3023451
[71]
Sutton R S, Barto A G. Reinforcement learning: an introduction. The MIT Press Cambridge, Massachusetts London, England, 1998, 712192
[72]
Naruse M, Mihana T, Hori H, et al. Scalable photonic reinforcement learning by time-division multiplexing of laser chaos. Sci Rep, 2018, 8(1), 10890 doi: 10.1038/s41598-018-29117-y
[73]
Ma Y T, Xiang S Y, Guo X X, et al. Time-delay signature concealment of chaos and ultrafast decision making in mutually coupled semiconductor lasers with a phase-modulated Sagnac loop. Opt Express, 2020, 28, 1665 doi: 10.1364/OE.384378
[74]
Han Y N, Xiang S Y, Wang Y, et al. Generation of multi-channel chaotic signals with time delay signature concealment and ultrafast photonic decision making based on globally-coupled semiconductor lasers network. Photonics Res, 2020, 8(11), 1792 doi: 10.1364/PRJ.403319
[75]
Zhou Z, Tu Z, Yin B, et al. Development trends in silicon photonics. Chin Opt Lett, 2013, 11(1), 012501 doi: 10.3788/COL201311.012501
[76]
Zhou Z P, Yin B, Michel J. On-chip light sources for silicon photonics. Light Sci Appl, 2015, 4, e358 doi: 10.1038/lsa.2015.131
[77]
Atabaki A H, Moazeni S, Pavanello F, et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature, 2018, 556, 349 doi: 10.1038/s41586-018-0028-z
[78]
Billah M R, Blaicher M, Hoose T, et al. Hybrid integration of silicon photonics circuits and InP lasers by photonic wire bonding. Optica, 2018, 5, 876 doi: 10.1364/OPTICA.5.000876
[79]
Guo X H, He A, Su Y K. Recent advances of heterogeneously integrated IIIV laser on Si. J Semicond, 2019, 40(10), 101304 doi: 10.1088/1674-4926/40/10/101304
[80]
Bai B W, Shu H W, Wang X J, et al. Towards silicon photonic neural networks for artificial intelligence. Sci China Inf Sci, 2020, 63(6), 160403 doi: 10.1007/s11432-020-2872-3
[81]
Bao S Y, Wang Y, Lina K, et al. A review of silicon-based wafer bonding processes, an approach to realize the monolithic integration of Si-CMOS and IIIV-on-Si wafers. J Semicond, 2020, in press
[82]
Ruan Z L, Zhu Y T, Chen P X, et al. Efficient hybrid integration of long-wavelength VCSELs on silicon photonic circuits. J Lightwave Technol, 2020, 38(18), 5100 doi: 10.1109/JLT.2020.2999526
[83]
Li Y Y, Wang Y, Yang D R, et al. Recent progress on optoelectronic synaptic devices. Sci Sin Inform, 2020, 50, 892 doi: 10.1360/SSI-2019-0248
[84]
Wetzstein G, Ozcan A, Gigan S, et al. Inference in artificial intelligence with deep optics and photonics. Nature, 2020, 588, 39 doi: 10.1038/s41586-020-2973-6

    • Search

    Advanced Search >>

    GET CITATION

    shu

    Export: BibTex EndNote

    Article Metrics

    Article views: 5625 Times PDF downloads: 427 Times Cited by: 0 Times

    History

    Received: 11 November 2020 Revised: 29 December 2020 Online: Accepted Manuscript: 14 January 2021Uncorrected proof: 14 January 2021Published: 08 February 2021

    Catalog

      Email This Article

      User name:
      Email:*请输入正确邮箱
      Code:*验证码错误
      Send
      Article Navigation >  Journal of Semiconductors  > 2021  >  42(2): 023105
      Shuiying Xiang, Yanan Han, Ziwei Song, Xingxing Guo, Yahui Zhang, Zhenxing Ren, Suhong Wang, Yuanting Ma, Weiwen Zou, Bowen Ma, Shaofu Xu, Jianji Dong, Hailong Zhou, Quansheng Ren, Tao Deng, Yan Liu, Genquan Han, Yue Hao. A review: Photonics devices, architectures, and algorithms for optical neural computing[J]. Journal of Semiconductors, 2021, 42(2): 023105. doi: 10.1088/1674-4926/42/2/023105 ****S Y Xiang, Y N Han, Z W Song, X X Guo, Y H Zhang, Z X Ren, S H Wang, Y T Ma, W W Zou, B W Ma, S F Xu, J J Dong, H L Zhou, Q S Ren, T Deng, Y Liu, G Q Han, Y Hao, A review: Photonics devices, architectures, and algorithms for optical neural computing[J]. J. Semicond., 2021, 42(2): 023105. doi: 10.1088/1674-4926/42/2/023105.
      Citation:
      Shuiying Xiang, Yanan Han, Ziwei Song, Xingxing Guo, Yahui Zhang, Zhenxing Ren, Suhong Wang, Yuanting Ma, Weiwen Zou, Bowen Ma, Shaofu Xu, Jianji Dong, Hailong Zhou, Quansheng Ren, Tao Deng, Yan Liu, Genquan Han, Yue Hao. A review: Photonics devices, architectures, and algorithms for optical neural computing[J]. Journal of Semiconductors, 2021, 42(2): 023105. doi: 10.1088/1674-4926/42/2/023105 ****
      S Y Xiang, Y N Han, Z W Song, X X Guo, Y H Zhang, Z X Ren, S H Wang, Y T Ma, W W Zou, B W Ma, S F Xu, J J Dong, H L Zhou, Q S Ren, T Deng, Y Liu, G Q Han, Y Hao, A review: Photonics devices, architectures, and algorithms for optical neural computing[J]. J. Semicond., 2021, 42(2): 023105. doi: 10.1088/1674-4926/42/2/023105.
      shu

      A review: Photonics devices, architectures, and algorithms for optical neural computing

      DOI: 10.1088/1674-4926/42/2/023105
      • 1.

        State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China

      • 2.

        State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi’an 710071, China

      • 3.

        State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (iMLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

      • 4.

        Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China

      • 5.

        School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

      • 6.

        School of Physical Science and Technology, Southwest University, Chongqing 400715, China

      More Information
      • Shuiying Xiang：was born in Ji’an, China, in 1986. She received the Ph.D. degree from Southwest Jiaotong University, Chengdu, China, in 2013. She is currently a Professor with State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China. She is the author or coauthor of more than 100 research papers. Her research interests include vertical cavity surface-emitting lasers, neuromorphic photonic systems, brain-inspired information processing, chaotic optical communication, and semiconductor lasers dynamics
      • Yue Hao：was born in the city of Chongqing, China, in 1958. He received the Ph.D. degree from Xi’an Jiao tong University, Xi'an, China, in 1991. He is currently a Professor at State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, the School of Microelectronics, Xidian University, Xi’an, China. His research interests include wide forbidden band semiconductor materials and devices, semiconductor device reliability physics and failure mechanism, terahertz semiconductor materials and device
      • Corresponding author: syxiang@xidian.edu.cn
      • Received Date: 2020-11-11
      • Revised Date: 2020-12-29
      • Published Date: 2021-02-10

      Catalog

        Journal of Semiconductors © 2017 All Rights Reserved   京ICP备05085259号-2

        /

        DownLoad:  Full-Size Img  PowerPoint
        Return
        Return