留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Perspective on photonic memristive neuromorphic computing

Elena Goi,  Qiming Zhang,  Xi Chen,  Haitao Luan,  Min Gu

downloadPDF
文章导航 > PhotoniX  > 2020 > 优先出版
Elena Goi, Qiming Zhang, Xi Chen, Haitao Luan, Min Gu. Perspective on photonic memristive neuromorphic computing[J]. PhotoniX. doi: 10.1186/s43074-020-0001-6
引用本文: Elena Goi, Qiming Zhang, Xi Chen, Haitao Luan, Min Gu. Perspective on photonic memristive neuromorphic computing[J]. PhotoniX. doi: 10.1186/s43074-020-0001-6
shu
Elena Goi, Qiming Zhang, Xi Chen, Haitao Luan, Min Gu. Perspective on photonic memristive neuromorphic computing[J]. PhotoniX. doi: 10.1186/s43074-020-0001-6
Citation: Elena Goi, Qiming Zhang, Xi Chen, Haitao Luan, Min Gu. Perspective on photonic memristive neuromorphic computing[J]. PhotoniX. doi: 10.1186/s43074-020-0001-6
shu

Perspective on photonic memristive neuromorphic computing

doi: 10.1186/s43074-020-0001-6

Perspective on photonic memristive neuromorphic computing

  • 1.

    Laboratory of Artificial-Intelligence Nanophotonics, School of Science, RMIT University, Melbourne, Victoria, 3001, Australia

  • 2.

    Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Funds: The authors thank all the members of the LAIN Laboratory for the inspiring discussions.

    摘要
  • 摘要: Neuromorphic computing applies concepts extracted from neuroscience to develop devices shaped like neural systems and achieve brain-like capacity and efficiency. In this way, neuromorphic machines, able to learn from the surrounding environment to deduce abstract concepts and to make decisions, promise to start a technological revolution transforming our society and our life. Current electronic implementations of neuromorphic architectures are still far from competing with their biological counterparts in terms of real-time information-processing capabilities, packing density and energy efficiency. A solution to this impasse is represented by the application of photonic principles to the neuromorphic domain creating in this way the field of neuromorphic photonics. This new field combines the advantages of photonics and neuromorphic architectures to build systems with high efficiency, high interconnectivity and high information density, and paves the way to ultrafast, power efficient and low cost and complex signal processing. In this Perspective, we review the rapid development of the neuromorphic computing field both in the electronic and in the photonic domain focusing on the role and the applications of memristors. We discuss the need and the possibility to conceive a photonic memristor and we offer a positive outlook on the challenges and opportunities for the ambitious goal of realising the next generation of full-optical neuromorphic hardware.
      关键词:
    •  / 
    •  / 
    •  / 
    •  / 
    •  / 
    •  
    Abstract: Neuromorphic computing applies concepts extracted from neuroscience to develop devices shaped like neural systems and achieve brain-like capacity and efficiency. In this way, neuromorphic machines, able to learn from the surrounding environment to deduce abstract concepts and to make decisions, promise to start a technological revolution transforming our society and our life. Current electronic implementations of neuromorphic architectures are still far from competing with their biological counterparts in terms of real-time information-processing capabilities, packing density and energy efficiency. A solution to this impasse is represented by the application of photonic principles to the neuromorphic domain creating in this way the field of neuromorphic photonics. This new field combines the advantages of photonics and neuromorphic architectures to build systems with high efficiency, high interconnectivity and high information density, and paves the way to ultrafast, power efficient and low cost and complex signal processing. In this Perspective, we review the rapid development of the neuromorphic computing field both in the electronic and in the photonic domain focusing on the role and the applications of memristors. We discuss the need and the possibility to conceive a photonic memristor and we offer a positive outlook on the challenges and opportunities for the ambitious goal of realising the next generation of full-optical neuromorphic hardware.
    • HTML全文
    • 参考文献(141)
  • [1] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity: the bulletin of mathematical biophysics. Bull Math Biophys. 1943;5:115–33.
    [2] O’Regan G. Artificial intelligence. A Br Hist Comput. 2012:229–52.
    [3] Intel Corporation. Neuromorphic computing, beyond today’s AI. Available at: www.intel.com.au/content/www/au/en/research/neuromorphic-computing.html. Accessed 12 June 2019.
    [4] Pedretti G, et al. Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity. Sci Rep. 2017;7:05480.
    [5] An H, Bai K, Yi Y. Advances in memristor neural networks - modeling and applications (ed. Calin Ciufudean); 2018.
    [6] An H, et al. “Opportunities and challenges on nanoscale 3D neuromorphic computing system.” 2017 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI). 2017:416–21.
    [7] Ielmini D, Wong HP, Article R. In-memory computing with resistive switching devices. Nat Electron. 2018;1:333–43.
    [8] Silver D, et al. Mastering the game of go with deep neural networks and tree search. Nature. 2016;529:484–8.
    [9] Mattheij J. Another way of looking at Lee Sedol vs AlphaGo. Hugo. 2016; Available at: jacquesmattheij.com. Accessed 13 June 2019.
    [10] Luo L. Principles of neurobiology. New York: Taylor & Francis Group; 2015.
    [11] Mead C. Neuromorphic electronic systems. Proc IEEE. 1990;78:1629–36.
    [12] Chicca E, et al. A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Trans Neural Netw. 2003;14:1297–307.
    [13] Jo SH, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 2010;10:1297–301.
    [14] Tait AN, Nahmias MA, Tian Y, Shastri BJ, Prucnal PR. Nanophotonic information physics: nanointelligence and nanophotonic computing (ed. Naruse, M.). Berlin Heidelberg: Springer; 2014. p. 183–222.
    [15] Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117:500–44.
    [16] Stein RB. A theoretical analysis of neuronal variability. Biophys J. 1965;5:173–94.
    [17] Hebb DO. The organisation of behavior. Berlin, Heidelberg: Wiley/Springer; 1949.
    [18] Furber S. Large-scale neuromorphic computing systems. J Neural Eng. 2016;13:051001.
    [19] Le Masson G, Renaud-Le Masson S, Debay D, Bal T. Feedback inhibition controls spike transfer in hybrid thalamic circuits. Nature. 2002;417:854–8.
    [20] Zou Q, et al. Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity. Netw Comput Neural Syst. 2006;17:211–33.
    [21] Park J, Yu T, Maier C, Joshi S, Cauwenberghs G. Live demonstration: hierarchical address-event routing architecture for reconfigurable large scale neuromorphic systems. In: ISCAS 2012–2012 IEEE Int. Symp Circuits Syst; 2012. p. 707–11.
    [22] Yu T, Park J, Joshi S, Maier C, Cauwenberghs G. 65K-Neuron integrate-and-fire array transceiver with address-event reconfigurable synaptic routing. In: 2012 IEEE Biomed. Circuits Syst. Conf. Intell. Biomed. Electron. Syst. Better Life Better Environ. BioCAS 2012 - Conf. Publ; 2012. p. 21–4.
    [23] Brandli C, Muller L, Delbruck T. Real-time, high-speed video decompression using a frame- and event-based DAVIS sensor. In: Proc. - IEEE Int. Symp. Circuits Syst; 2014. p. 686–9.
    [24] Yang M, Chien CH, Delbruck T, Liu SC. A 0.5 V 55 μW 64 × 2 channel binaural silicon cochlea for event-driven stereo-audio sensing. IEEE J Solid State Circuits. 2016;51:2554–69.
    [25] Indiveri G, Corradi F, Qiao N. Neuromorphic architectures for spiking deep neural networks. Tech Dig - Int Electron Devices Meet IEDM. 2015;15:68–71.
    [26] Sawada J, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science. 2014;345:668–73.
    [27] Mostafa H, Müller LK, Indiveri G. An event-based architecture for solving constraint satisfaction problems. Nat Commun. 2015;6:9941.
    [28] Chicca E, Stefanini F, Bartolozzi C, Indiveri G. Neuromorphic electronic circuits for building autonomous cognitive systems. Proc IEEE. 2014;102:1367–88.
    [29] Mayr CG, Sheik S, Bartolozzi C, Chicca E. Editorial : synaptic plasticity for neuromorphic systems. Front Neurosci. 2016;10:214.
    [30] Wang Z, et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat Electron. 2018;1:137–45.
    [31] Chua L. Memristor-The missing circuit element. IEEE Trans Circuit Theory. 1971;18:507–19.
    [32] Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature. 2008;453:80–4.
    [33] Upadhyaya HM, Chandra S. Polarity-dependent memory switching effects in the Ti/CdxPb 1-xS/Ag system. Semicond Sci Technol. 1995;10:332–8.
    [34] Lau CN, Stewart DR, Williams RS, Bockrath M. Direct observation of nanoscale switching centers in metal/molecule/metal structures. Nano Lett. 2004;4:569–72.
    [35] Waser R, Aono M. Nanoionics-based resistive switching memories. Nat Mater. 2007;6:833–40.
    [36] Pershin YV, Di Ventra M. Spin memristive systems: spin memory effects in semiconductor spintronics. Phys Rev B - Condens Matter Mater Phys. 2008;78:5–8.
    [37] Wu X, et al. Reproducible unipolar resistance switching in stoichiometric ZrO2 films. Appl Phys Lett. 2007;90:11–4.
    [38] Chua LO, Kang SM. Memristive devices and systems. Proc IEEE. 1976;64:209–23.
    [39] HP Labs. HP Memristor FAQ. Hewlett-Packard development company, L.P. (2009). Available at: https://www.hpl.hp.com/news/2008/apr-jun/memristor_faq.html. Accessed: 18 June 2019.
    [40] Saïghi S, et al. Plasticity in memristive devices for spiking neural networks. Front Neurosci. 2015;9:51.
    [41] Thomas A, et al. Tunnel junction based memristors as artificial synapses. Front Neurosci. 2015;9:241.
    [42] Wang Z, Ambrogio S, Balatti S, Ielmini D. A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems. Front Neurosci. 2015;9:438.
    [43] Chanthbouala A, et al. A ferroelectric memristor. Nat Mater. 2012;11:860.
    [44] Ohno T, et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater. 2011;10:591.
    [45] Alibart F, Zamanidoost E, Strukov DB. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun. 2013;4:2072.
    [46] Eryilmaz SB, et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front Neurosci. 2014;8:205.
    [47] Prezioso M, Hoskins BD, Adam GC, Likharev KK, Strukov DB. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature. 2015;521:62–4.
    [48] Pickett MD, Medeiros-ribeiro G, Williams RS. A scalable neuristor built with Mott memristors. Nat Mater. 2013;12:10–3.
    [49] Lim H, et al. Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study. Sci Rep. 2015;5:9776.
    [50] Stoliar P, et al. A leaky-integrate-and-fire neuron analog realized with a Mott insulator. Adv Funct Mater. 2017;27:1604740.
    [51] Tuma T, Pantazi A, Le Gallo M, Sebastian A, Eleftheriou E. Stochastic phase-change neurons. Nat Nanotechnol. 2016;11:693.
    [52] Lim H, et al. Relaxation oscillator-realized artificial electronic neurons, their responses, and noise. Nanoscale. 2016;8:9629–40.
    [53] Adam GC, Khiat A, Prodromakis T. Challenges hindering memristive neuromorphic hardware from going mainstream. Nat Commun. 2018;9:5267.
    [54] Ferreira De Lima T, Shastri BJ, Tait AN, Nahmias MA, Prucnal PR. Progress in neuromorphic photonics. Nanophotonics. 2017;6:577–99.
    [55] Caulfield HJ, Dolev S. Why future supercomputing requires optics. Nat Photonics. 2010;4:261–3.
    [56] Shastri BJ, et al. Neuromorphic photonics, principles of. Berlin Heidelberg: Springer; 2018.
    [57] Psaltis D, Farhat N. Optical information processing based on an associative-memory model of neural nets with thresholding and feedback. Opt Lett. 2008;10:98–100.
    [58] Abu-Mostafa YS, Psaltis D. Optical neural computers. Sci Am. 1987;256:88–95.
    [59] Jutamulia S, Yu FTS. Overview of the hybrid optical neural networks. Opt Laser Technol. 1996;28:59–72.
    [60] Hill MT, Frietman EEE, De Waardt H, Khoe GD, Dorren HJS. All fiber-optic neural network using coupled SOA based ring lasers. IEEE Trans Neural Netw. 2002;13:1504–13.
    [61] Rosenbluth D, Kravtsov K, Fok MP, Prucnal PR. A high performance photonic pulse processing device. Opt Express. 2009;17:22767.
    [62] Fok MP, et al. Signal feature recognition based on lightwave neuromorphic signal processing. Opt Lett. 2011;36:19–21.
    [63] Kravtsov KS, Fok MP, Prucnal PR, Rosenbluth D. Ultrafast all-optical implementation of a leaky integrate-and-fire neuron. Opt Express. 2011;19:2133.
    [64] Hughes TW, Minkov M, Shi Y, Fan S. Training of photonic neural networks through in situ backpropagation. Optica. 2018;5:864–71.
    [65] Nahmias MA, Shastri BJ, Ferreira De Lima T, Tait AN. Neuromorphic photonics. Opt Photonics News. 2018;29:36–41.
    [66] Zhang Q, Yu H, Barbiero M, Wang B, Gu M. Artificial neural networks enabled by nanophotonics. Light Sci Appl. 2019;8:42.
    [67] Fok MP, Tian Y, Rosenbluth D, Prucnal PR. Asynchronous spiking photonic neuron for lightwave neuromorphic signal processing. Opt Lett. 2012;37:3309–11.
    [68] Gholipour B, et al. Amorphous metal-sulphide microfibers enable photonic synapses for brain-like computing. Adv Opt Mater. 2015;3:635–41.
    [69] Agnus G, et al. Two-terminal carbon nanotube programmable devices for adaptive architectures. Adv Mater. 2010;22:702–6.
    [70] Cheng Z, Ríos C, Pernice WHP, Wright CD, Bhaskaran H. On-chip photonic synapse. Sci Adv. 2017;3:e1700160.
    [71] Chakraborty I, Saha G, Sengupta A, Roy K. Toward fast neural computing using all-photonic phase change spiking neurons. Sci Rep. 2018;8:12980.
    [72] Feldmann J, Youngblood N, Wright CD, Bhaskaran H, Pernice WHP. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature. 2019;569:208–14.
    [73] Shen Y, Harris NC, Englund D, Soljacic M. Deep learning with coherent nanophotonic circuits. Nat Photonics. 2017;11:441–7.
    [74] Tait AN, et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci Rep. 2017;7:7430.
    [75] Koch U, Hoessbacher C, Emboras A, Leuthold J. Optical memristive switches. J Electroceram. 2017;39:239–50.
    [76] Emboras A, et al. Nanoscale plasmonic memristor with optical readout functionality. Nano Lett. 2013;13:6151–5.
    [77] Wu H, Zhou J, Lan C, Guo Y, Bi K. Microwave memristive-like nonlinearity in a dielectric metamaterial. Sci Rep. 2014;4:5499.
    [78] McCall SL. Instabilities in continuous-wave light propagation in absorbing media. Phys Rev A. 1974;9:1515–23.
    [79] Gibbs HM, McCall SL, Venkatesan TNC. Differential gain and bistability using a sodium-filled fabry-perot interferometer. Phys Rev Lett. 1976;36:1135–8.
    [80] Sharif MA, Majles Ara MH, Ghafary B, Salmani S, Mohajer S. Experimental observation of low threshold optical bistability in exfoliated graphene with low oxidation degree. Opt Mater (Amst). 2016;53:80–6.
    [81] Liu Z, et al. Nonlinear optical properties of graphene oxide in nanosecond and picosecond regimes. Appl Phys Lett. 2009;94:21902.
    [82] Castro Neto AH, Peres NMR, Novoselov KS, Geim AK. The electronic properties of graphene. Rev Mod Phys. 2009;81:109–62.
    [83] Bonaccorso F, Sun Z, Hasan T, Ferrari AC. Graphene photonics and optoelectronics. Nat Photonics. 2010;4:611–22.
    [84] Dremetsika E, et al. Measuring the nonlinear refractive index of graphene using the optical kerr effect method. Opt Lett. 2016;41:3281–4.
    [85] Peres NMR, Bludov YV, Santos JE, Jauho AP, Vasilevskiy MI. Optical bistability of graphene in the terahertz range. Phys Rev B - Condens Matter Mater Phys. 2014;90:125425.
    [86] Yao X, Belyanin A. Giant optical nonlinearity of Graphene in a strong magnetic field. Phys Rev Lett. 2012;108:255503.
    [87] Zhao F, et al. Functionalized graphitic carbon nitride for metal-free, flexible and rewritable nonvolatile memory device via direct laser-writing. Sci Rep. 2014;4:5882.
    [88] De Silva KKH, Huang HH, Joshi RK, Yoshimura M. Chemical reduction of graphene oxide using green reductants. Carbon N Y. 2017;119:190–9.
    [89] Zheng X, Jia B, Chen X, Gu M. In situ third-order non-linear responses during laser reduction of graphene oxide thin films towards on-chip non-linear photonic devices. Adv Mater. 2014;26:2699–703.
    [90] Tian H, et al. Cost-effective, transfer-free, flexible resistive random access memory using laser-scribed reduced graphene oxide patterning technology. Nano Lett. 2014;14:3214–9.
    [91] Wang Y, Mi H, Zheng Q, Ma Z, Gong S. Graphene/phase change material nanocomposites: light-driven, reversible electrical resistivity regulation via form-stable phase transitions. ACS Appl Mater Interfaces. 2015;7:2641–7.
    [92] Miller KJ, Haglund RF, Weiss SM. Optical phase change materials in integrated silicon photonic devices: review. Opt Mater Express. 2018;8:2415.
    [93] Cheng Z, et al. Device-level photonic memories and logic applications. Adv Mater. 2018;30:1802435.
    [94] Zhang W, Mazzarello R, Wuttig M, Ma E. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing. Nat Rev Mater. 2019;4:150–68.
    [95] Feldmann J, et al. Calculating with light using a chip-scale all-optical abacus. Nat Commun. 2017;8:1256.
    [96] Ríos C, et al. Integrated all-photonic non-volatile multi-level memory. Nat Photonics. 2015;9:725–32.
    [97] Rios C, et al. Controlled switching of phase-change materials by evanescent-field coupling in integrated photonics [invited]. Opt Mater Express. 2018;8:2455.
    [98] Zheng J, et al. GST-on-silicon hybrid nanophotonic integrated circuits: a non-volatile quasi-continuously reprogrammable platform. Opt Mater Express. 2018;8:1551.
    [99] Liu S, Wei J, Gan F. Optical nonlinear absorption characteristics of crystalline Ge 2Sb2Te5 thin films. J Appl Phys. 2011;110:33503.
    [100] Muramoto K, et al. VO2-dispersed glass: a new class of phase change material. Sci Rep. 2018;8:2275.
    [101] Lopez R, Haglund RF, Feldman LC, Boatner LA, Haynes TE. Optical nonlinearities in VO 2 nanoparticles and thin films. Appl Phys Lett. 2004;85:5191–3.
    [102] Kürüm U, et al. The third order nonlinear optical characteristics of amorphous vanadium oxide thin film. Appl Phys A Mater Sci Process. 2011;104:1025–30.
    [103] Peng C, Cheng L, Mansuripur M. Experimental and theoretical investigations of laser-induced crystallization and amorphization in phase-change optical recording media. J Appl Phys. 1997;82:4183–91.
    [104] Sharma N, Sharda S, Katyal SC, Sharma V, Sharma P. Effect of Te on linear and non-linear optical properties of new quaternary Ge-se-Sb-Te chalcogenide glasses. Electron Mater Lett. 2014;10:101–6.
    [105] Wen S, Meng Y, Jiang M, Wang Y. Multi-level coding-recoding by ultrafast phase transition on Ge2Sb2Te5 thin films. Sci Rep. 2018;8:4979.
    [106] Wuttig M, Yamada N. Phase-change materials for rewriteable data storage. Nat Mater. 2007;6:824–33.
    [107] Goi E, Mashford BS, Cumming BP, Gu M. Tuning the refractive index in gyroid photonic crystals via lead-chalcogenide nanocrystal coating. Adv Opt Mater. 2016;4:226–30.
    [108] Goi E, Yue Z, Cumming BP, Gu M. A layered-composite nanometric Sb2Te3 material for chiral photonic bandgap engineering. Phys Status Solidi A. 2018;215:1800152.
    [109] Yue Z, Xue G, Liu J, Wang Y, Gu M. Nanometric holograms based on a topological insulator material. Nat Commun. 2017;8:15354.
    [110] Goi E, Yue Z, Cumming BP, Gu M. Observation of type I photonic Weyl points in optical frequencies. Laser Photonics Rev. 2018;12:1700271.
    [111] Liu S, Wei J, Gan F. Nonlinear absorption of Sb-based phase change materials due to the weakening of the resonant bond. Appl Phys Lett. 2012;100:111903.
    [112] Liu J, Liu S, Wei J. Origin of the giant optical nonlinearity of Sb2 Te3 phase change materials. Appl Phys Lett. 2010;97:261903.
    [113] Leuthold J, Koos C, Freude W. Nonlinear silicon photonics. Nat Photonics. 2010;4:535–44.
    [114] Nozaki K, et al. Ultralow-power all-optical RAM based on nanocavities. Nat Photonics. 2012;6:248–52.
    [115] Polyanskiy, M. N. Refractiveindex.info. Available at: https://refractiveindex.info. Accessed: 13 June 2019.
    [116] Schmiedova V, et al. Physical properties investigation of reduced graphene oxide thin films prepared by material inkjet printing. J Nanomater. 2017;55:3501903.
    [117] Kravets VG, et al. Engineering optical properties of a graphene oxide metamaterial assembled in microfluidic channels. Opt Express. 2015;23:1265.
    [118] Zheng X, Jia B, Chen X, Gu M. Giant optical nonlinear response of graphene oxide films. In: Frontiers in Optics 2013 FW6C.5; 2013.
    [119] Yue M, Si J, Yan L, Yu Y, Hou X. Enhanced nonlinear optical properties of reduced graphene oxide decorated with silver nanoparticles. Opt Mater Express. 2018;8:698–703.
    [120] Humlicek J, Lukes F, Schmidt E. Handbook of Optical Constants of Solids (ed. PALIK, E. D.): Academic Press. California; 1998. p. 607–36.
    [121] Koos C, Jacome L, Poulton C, Leuthold J, Freude W. Nonlinear silicon-on-insulator waveguides for all-optical signal processing. Opt Express. 2007;15:5976–90.
    [122] Chu CH, et al. Active dielectric metasurface based on phase-change medium. Laser Photonics Rev. 2016;10:986–94.
    [123] Van Bilzen B, et al. Production of VO2 thin films through post-deposition annealing of V2O3 and VOx films. Thin Solid Films. 2015;591:143–8.
    [124] Laniel JM, Hô N, Vallée R, Villeneuve A. Nonlinear-refractive-index measurement in As2S3 channel waveguides by asymmetric self-phase modulation. J Opt Soc Am B. 2005;22:437–45.
    [125] Boguslawski J, Sobon G, Zybala R, Sotor J. Dissipative soliton generation in Er-doped fiber laser mode-locked by Sb2Te3 topological insulator. Opt Lett. 2015;40:2786–9.
    [126] Wang J, Long Y. On-chip silicon photonic signaling and processing: a review. Sci Bull. 2018;63:1267–310.
    [127] Ríos C, et al. In-memory computing on a photonic platform. Sci Adv. 2018;5:5759.
    [128] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.
    [129] Lin X, et al. All-optical machine learning using diffractive deep neural networks. Science. 2018;361:1004–8.
    [130] Goi E, Gu M. "Laser printing of a nano-imager to perform full optical machine learning," in 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, OSA Technical Digest. Washington, D.C.: Optical Society of America; 2019. paper jsi_p_3.
    [131] Gu M, Fang X, Ren H, Goi E. Optically digitalized holography: a perspective for all-optical machine learning. Engineering. 2019;5:363–65.
    [132] Cheng C, Liu K, Xiang B, Suh J, Wu J. Ultra-long, free-standing, single-crystalline vanadium dioxide micro/nanowires grown by simple thermal evaporation. Appl Phys Lett. 2012;100:103111.
    [133] Hemanadhan M, Bapanayya C, Agarwal SC. Simple flash evaporator for making thin films of compounds. J Vac Sci Technol A. 2010;65:62–626.
    [134] Miyake M, Chen Y-C, Braun PV, Wiltzius P. Fabrication of three-dimensional photonic crystals using multibeam interference lithography and electrodeposition. Adv Mater. 2009;21:3012–5.
    [135] Zheng X, et al. Highly efficient and ultra-broadband graphene oxide ultrathin lenses with three-dimensional subwavelength focusing. Nat Commun. 2015;6:8433.
    [136] Gan Z, Cao Y, Evans R a, Gu M. Three-dimensional deep sub-diffraction optical beam lithography with 9 nm feature size. Nat Commun. 4:2061, 2013.
    [137] Cao Y, Gan Z, Jia B, Evans RA, Gu M. High-photosensitive resin for super-resolution direct-laser-writing based on photoinhibited polymerization. Opt Express. 2011;19:19486–94.
    [138] Juodkazis S, et al. Sculpturing of photonic crystals by ion beam lithography: towards complete photonic bandgap at visible wavelengths. Opt Express. 2011;19:5802–10.
    [139] Qi M, et al. A three-dimensional optical photonic crystal with designed point defects. Nature. 2004;429:538–42.
    [140] Goi E, Cumming B, Gu M. Impact of cubic symmetry on optical activity of dielectric 8-srs networks. Appl Sci. 2018;8:2104.
    [141] García-Santamaría F, et al. A germanium inverse woodpile structure with a large photonic band gap. Adv Mater. 2007;19:1567–70.
    • 相关文章
    • 施引文献
  • 资源附件(0)
WeChat 点击查看大图
计量
  • 文章访问数:  362
  • HTML全文浏览量:  5
  • PDF下载量:  24
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-07-24
  • 录用日期:  2019-11-04
  • 网络出版日期:  2020-03-03

目录

    /

    返回文章
    返回

    PhotoniX

    联系我们

    • 地址： 北京丰台区海鹰路1号院6楼
    • 邮政编码： 100070
    • 电话： 010-63728336，022-58168516

    友情链接

    Copyright © PhotoniX    京ICP备15010856号-13

    本系统由 北京仁和汇智信息技术有限公司 开发