• Journal of Semiconductors
  • Vol. 42, Issue 2, 023105 (2021)
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
  • 1State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China
  • 2State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi’an 710071, China
  • 3State 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
  • 4Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
  • 5School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
  • 6School of Physical Science and Technology, Southwest University, Chongqing 400715, China
  • show less
    DOI: 10.1088/1674-4926/42/2/023105 Cite this Article
    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 Copy Citation Text show less
    References

    [1] G E Moore. Cramming more components onto integrated circuits. Electron, 38, 114(1965).

    [2] M M Waldrop. The chips are down for Moore’s law. Nat News, 530, 144(2016).

    [3] W Maass. Networks of spiking neurons: The third generation of neural network models. Neur Netw, 10, 1659(1997).

    [4] Z F Mainen, T J Sejnowski. Reliability of spike timing in neocortical neurons. Science, 268, 1503(1995).

    [5] J J Hopfield. Pattern recognition computation using action potential timing for stimulus representation. Nature, 376, 33(1995).

    [6] G Q Bi, M M Poo. Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci, 18, 10464(1998).

    [7] L F Abbott, S B Nelson. Synaptic plasticity: Taming the beast. Nat Neurosci, 3, 1178(2000).

    [8] G Q Bi, M M Poo. Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu Rev Neurosci, 24, 139(2001).

    [9]

    [10] K Roy, A Jaiswal, P Panda. Towards spike-based machine intelligence with neuromorphic computing. Nature, 575, 607(2019).

    [11] J D Zhu, T Zhang, Y C Yang et al. A comprehensive review on emerging artificial neuromorphic devices. Appl Phys Rev, 7, 011312(2020).

    [12] W Zhang, B Gao, J Tang et al. Neuro-inspired computing chips. Nat Electron, 3, 371(2020).

    [13] P R Prucnal, B J Shastri, T F de Lima et al. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv Opt Photon, 8, 228(2016).

    [14] M A Nahmias, B J Shastri, A N Tait et al. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE J Sel Top Quantum Electron, 19, 1800212(2013).

    [15] B Gholipour, P Bastock, C Craig et al. Amorphous metal-sulphide microfibers enable photonic synapses for brain-like computing. Adv Opt Mater, 5, 635(2015).

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

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

    [18] X Zhuge, J Wang, F Zhuge. Photonic synapses for ultrahigh-speed neuromorphic computing. Phys Status Solidi RRL, 13, 1900082(2019).

    [19] T F de Lima, H T Peng, A N Tait et al. Machine learning with neuromorphic photonics. J Lightwave Technol, 37, 1515(2019).

    [20] W W Zou, B W Ma, S F Xu et al. Towards an intelligent photonic system. Sci China Inform Sci, 63, 160401(2020).

    [21]

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

    [23] W Coomans, L Gelens, S Beri et al. Solitary and coupled semiconductor ring lasers as optical spiking neurons. Phys Rev E, 84, 036209(2011).

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

    [25] 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, 1504109(2016).

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

    [27] S Y Xiang, Y 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, 1700207(2017).

    [28] 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).

    [29] 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(2018).

    [30] T Deng, J Robertson, Z M Wu et al. Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks. IEEE Access, 6, 67951(2018).

    [31] 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).

    [32] 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).

    [33] Y H Zhang, S Y Xiang, X 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(2019).

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

    [35] 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).

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

    [37] B W Ma, W W Zou. 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, 63, 160408(2020).

    [38]

    [39]

    [40] R Toole, M P Fok. Photonic implementation of a neuronal algorithm applicable towards angle of arrival detection and localization. Opt Express, 23, 16133(2015).

    [41] Q S Ren, Y L Zhang, R Wang et al. Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation. Opt Express, 23, 25247(2015).

    [42] R Toole, A N Tait, T F de Lima et al. Photonic implementation of spike-timing-dependent plasticity and learning algorithms of biological neural systems. J Lightwave Technol, 34, 470(2016).

    [43] Q Li, Z Wang, Y S Le et al. Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier. Proc SPIE, 10019, 2245976(2016).

    [44] 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).

    [45] T Lima, B J Shastri, A N Tait et al. Progress in neuromorphic photonics. Nanophotonics, 6, 577(2017).

    [46] S Song, J Kim, S M Kwon et al. Recent progress of optoelectronic and all-optical neuromorphic devices: a comprehensive review of device structures, materials, and applications. Adv Intell Syst, 2000119(2020).

    [47] 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 Inform Sci, 63, 160405(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] 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(2020).

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

    [51] 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(2020).

    [52] S F Xu, J Wang, R Wang et al. High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays. Opt Express, 27, 19778(2019).

    [53] S F Xu, J Wang, W W Zou. Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines. Opt Lett, 45, 3689(2020).

    [54] S F Xu, X T Zou, B W Ma et al. Deep-learning-powered photonic analog-to digital conversion. Light Sci Appl, 8, 66(2019).

    [55] H L Zhou, Y H Zhao, G X Xu et al. Chip-scale optical matrix computation for PageRank algorithm. IEEE J Sel Top Quantum Electron, 26, 8300910(2020).

    [56]

    [57] H L Zhou, Y H Zhao, Y X Wei et al. All-in-one silicon photonic polarization processor. Nanophotonics, 8, 2257(2019).

    [58]

    [59] H L Zhou, Y H Zhao, X Wang et al. Self-configuring and reconfigurable silicon photonic signal processor. ACS Photonics, 7, 792(2020).

    [60] W Maass, T Natschlager, H Markram. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neur Comput, 14, 2531(2002).

    [61] W Maass, T Natschlager, H Markram. Fading memory and kernel properties of generic cortical microcircuit models. J Physiol-Paris, 98, 315(2004).

    [62] M Lukosevicius, H Jaeger. Reservoir computing approaches to recurrent neural network training. Comput Sci Rev, 3, 127(2009).

    [63] V D S Guy, D Brunner, M C Soriano. Advances in photonic reservoir computing. Nanophotonics, 6, 561(2017).

    [64] D Brunner, B Penkovsky, B A Marquez et al. Tutorial: Photonic neural networks in delay systems. J Appl Phys, 124, 152004(2018).

    [65] G Tanaka, T Yamane, J B Héroux et al. Recent advances in physical reservoir computing: A review. Neur Netw, 115, 100(2019).

    [66] X X Guo, S Y Xiang, Y H Zhang et al. Polarization multiplexing reservoir computing based on a VCSEL with polarized optical feedback. IEEE J Sel Top Quantum Electron, 26, 1700109(2020).

    [67] X X Guo, S Y Xiang, Y H Zhang et al. Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system. Opt Express, 27, 23293(2019).

    [68] X X Guo, S Y Xiang, Y H Zhang et al. Enhanced memory capacity of a neuromorphic reservoir computing system based on a VCSEL with double optical feedbacks. Sci China Inf Sci, 63, 160407(2020).

    [69] X X Guo, S Y Xiang, Y H Zhang et al. High-speed neuromorphic reservoir computing based on a semiconductor nanolaser with optical feedback under electrical modulation. IEEE J Sel Top Quantum Electron, 26, 1500707(2020).

    [70] X X Guo, S Y Xiang, Qu Y. et al. Enhanced prediction performanceof a neuromorphic reservoir computing using a semiconductor nanolaser with double phase conjugate feedbacks. J Lightwave Technol, 39, 129(2021).

    [71]

    [72] M Naruse, T Mihana, H Hori et al. Scalable photonic reinforcement learning by time-division multiplexing of laser chaos. Sci Rep, 8, 10890(2018).

    [73] Y T Ma, S Y Xiang, X X Guo 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, 28, 1665(2020).

    [74] Y N Han, S Y Xiang, Y Wang 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, 8, 1792(2020).

    [75] Z Zhou, Z Tu, B Yin et al. Development trends in silicon photonics. Chin Opt Lett, 11, 012501(2013).

    [76] Z P Zhou, B Yin, J Michel. On-chip light sources for silicon photonics. Light Sci Appl, 4, e358(2015).

    [77] A H Atabaki, S Moazeni, F Pavanello et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature, 556, 349(2018).

    [78] M R Billah, M Blaicher, T Hoose et al. Hybrid integration of silicon photonics circuits and InP lasers by photonic wire bonding. Optica, 5, 876(2018).

    [79] X H Guo, A He, Y K Su. Recent advances of heterogeneously integrated IIIV laser on Si. J Semicond, 40, 101304(2019).

    [80] B W Bai, H W Shu, X J Wang et al. Towards silicon photonic neural networks for artificial intelligence. Sci China Inf Sci, 63, 160403(2020).

    [81]

    [82] Z L Ruan, Y T Zhu, P X Chen et al. Efficient hybrid integration of long-wavelength VCSELs on silicon photonic circuits. J Lightwave Technol, 38, 5100(2020).

    [83] Y Y Li, Y Wang, D R Yang et al. Recent progress on optoelectronic synaptic devices. Sci Sin Inform, 50, 892(2020).

    [84] G Wetzstein, A Ozcan, S Gigan et al. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39(2020).

    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
    Download Citation