J. Semicond. > 2021, Volume 42 > Issue 1 > Article Number: 013105

Neuromorphic vision sensors: Principle, progress and perspectives

Fuyou Liao 1, 2, , Feichi Zhou 2, and Yang Chai 1, 2, ,

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  • Corresponding author: Yang Chai, e-mail: ychai@polyu.edu.hk
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    Abstract: Conventional frame-based image sensors suffer greatly from high energy consumption and latency. Mimicking neurobiological structures and functionalities of the retina provides a promising way to build a neuromorphic vision sensor with highly efficient image processing. In this review article, we will start with a brief introduction to explain the working mechanism and the challenges of conventional frame-based image sensors, and introduce the structure and functions of biological retina. In the main section, we will overview recent developments in neuromorphic vision sensors, including the silicon retina based on conventional Si CMOS digital technologies, and the neuromorphic vision sensors with the implementation of emerging devices. Finally, we will provide a brief outline of the prospects and outlook for the development of this field.

    Key words: image sensorssilicon retinaneuromorphic vision sensorsphotonic synapses



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    Yu H, Gong J, Wei H, et al. Mixed-halide perovskite for ultrasensitive two-terminal artificial synaptic devices. Mater Chem Front, 2019, 3(5), 941

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    Etienne-Cummings R, Van der Spiegel J. Neuromorphic vision sensors. Sens Actuators A, 1996, 56(1/2), 19

    [42]

    Indiveri G, Douglas R. Neuromorphic vision sensors. Science, 2000, 288(5469), 1189

    [43]

    Moini A. Vision chips. Springer Science & Business Media, 1999

    [44]

    Lichtsteiner P, Posch C, Delbruck T. A 128 × 128 120 db 30 mW asynchronous vision sensor that responds to relative intensity change. 2006 IEEE International Solid State Circuits Conference – Digest of Technical Papers, 2006, 2060

    [45]

    Lichtsteiner P, Posch C, Delbruck T. A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor. IEEE J Solid-State Circuits, 2008, 43(2), 566

    [46]

    Leñero-Bardallo J A, Serrano-Gotarredona T, Linares-Barranco B. A 3.6 μs latency asynchronous frame-free event-driven dynamic-vision-sensor. IEEE J Solid-State Circuits, 2011, 46(6), 1443

    [47]

    Posch C, Matolin D, Wohlgenannt R. A QVGA 143 dB dynamic range frame-free pwm image sensor with lossless pixel-level video compression and time-domain CDS. IEEE J Solid-State Circuits, 2011, 46(1), 259

    [48]

    Posch C, Matolin D, Wohlgenannt R. A QVGA 143dB dynamic range asynchronous address-event PWM dynamic image sensor with lossless pixel-level video compression. 2010 IEEE International Solid-State Circuits Conference (ISSCC), 2010, 400

    [49]

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    [50]

    Brandli C, Berner R, Yang M, et al. A 240 × 180 130 dB 3 μs latency global shutter spatiotemporal vision sensor. IEEE J Solid-State Circuits, 2014, 49(10), 2333

    [51]

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    Roy K, Padmanabhan M, Goswami S, et al. Graphene–MoS2 hybrid structures for multifunctional photoresponsive memory devices. Nat Nanotechnol, 2013, 8(11), 826

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    Xiang D, Liu T, Xu J, et al. Two-dimensional multibit optoelectronic memory with broadband spectrum distinction. Nat Commun, 2018, 9(1), 2966

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    Qin S, Wang F, Liu Y, et al. A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater, 2017, 4(3), 035022

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    Ni Z, Wang Y, Liu L, et al. Hybrid structure of silicon nanocrystals and 2D WSe2 for broadband optoelectronic synaptic devices. 2018 IEEE International Electron Devices Meeting (IEDM), 2018, 38.5.1

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    Wang W, Panin G N, Fu X, et al. MoS2 memristor with photoresistive switching. Sci Rep, 2016, 6(1), 31224

    [62]

    Tran M D, Kim H, Kim J S, et al. Two-terminal multibit optical memory via van der Waals heterostructure. Adv Mater, 2019, 31(7), 1807075

    [63]

    Hu L, Yuan J, Ren Y, et al. Phosphorene/ZnO nano-heterojunctions for broadband photonic nonvolatile memory applications. Adv Mater, 2018, 30(30), 1801232

    [64]

    Zhou F, Chen J, Tao X, et al. 2D materials based optoelectronic memory: Convergence of electronic memory and optical sensor. Research, 2019, 2019, 9490413

    [65]

    Chai Y. In-sensor computing for machine vision. Nature, 2020, 579, 32

    [66]

    Zhang K, Jung Y H, Mikael S, et al. Origami silicon optoelectronics for hemispherical electronic eye systems. Nat Commun, 2017, 8(1), 1782

    [67]

    Laughlin S B, van Steveninck R R R, Anderson J C. The metabolic cost of neural information. Nat Neurosci, 1998, 1(1), 36

    [1]

    McCulloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys, 1943, 5(4), 115

    [2]

    Hebb D O. The organization of behavior: a neuropsychological theory. J Wiley; Chapman & Hall, 1949

    [3]

    Carver M. Analog VLSI and neural systems. Addison-Wesley, 1989

    [4]

    Maher M A C, Deweerth S P, Mahowald M A, et al. Implementing neural architectures using analog VLSI circuits. IEEE Trans Circuits Syst, 1989, 36(5), 643

    [5]

    Mead C. Neuromorphic electronic systems. Proc IEEE, 1990, 78(10), 1629

    [6]

    Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol, 1952, 117(4), 500

    [7]

    Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345(6197), 668

    [8]

    Zidan M A, Strachan J P, Lu W D. The future of electronics based on memristive systems. Nat Electron, 2018, 1(1), 22

    [9]

    Wang C Y, Wang C, Meng F, et al. 2D layered materials for memristive and neuromorphic applications. Adv Electron Mater, 2019, 6(2), 1901107

    [10]

    Tuma T, Pantazi A, Le Gallo M, et al. Stochastic phase-change neurons. Nat Nanotechnol, 2016, 11(8), 693

    [11]

    Kuzum D, Jeyasingh R G D, Lee B, et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett, 2012, 12(5), 2179

    [12]

    Yang C S, Shang D S, Liu N, et al. A synaptic transistor based on quasi-2D molybdenum oxide. Adv Mater, 2017, 29(27), 1700906

    [13]

    Tian H, Guo Q, Xie Y, et al. Anisotropic black phosphorus synaptic device for neuromorphic applications. Adv Mater, 2016, 28(25), 4991

    [14]

    Takeo O, Tsuyoshi H, Tohru T, et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater, 2011, 10(8), 591

    [15]

    Posch C, Serrano-Gotarredona T, Linares-Barranco B, et al. Retinomorphic event-based vision sensors: bioinspired cameras with spiking output. Proc IEEE, 2014, 102(10), 1470

    [16]

    Steffen L, Reichard D, Weinland J, et al. Neuromorphic stereo vision: A survey of bio-inspired sensors and algorithms. Front Neurorobot, 2019, 13, 28

    [17]

    Zhou F, Zhou Z, Chen J, et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat Nanotechnol, 2019, 14(8), 776

    [18]

    Mennel L, Symonowicz J, Wachter S, et al. Ultrafast machine vision with 2D material neural network image sensors. Nature, 2020, 579(7797), 62

    [19]

    Wang C Y, Liang S J, Wang S, et al. Gate-tunable van der Waals heterostructure for reconfigurable neural network vision sensor. Sci Adv, 2020, 6(26), eaba6173

    [20]

    Seo S, Jo S H, Kim S, et al. Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat Commun, 2018, 9(1), 1

    [21]

    Tian H, Wang X, Wu F, et al. High performance 2D perovskite/ graphene optical synapses as artificial eyes. 2018 IEEE International Electron Devices Meeting (IEDM), 2018, 38.6.1

    [22]

    Choi C, Choi M K, Liu S, et al. Human eye-inspired soft optoelectronic device using high-density MoS2-graphene curved image sensor array. Nat Commun, 2017, 8(1), 1

    [23]

    Gu L, Poddar S, Lin Y, et al. A biomimetic eye with a hemispherical perovskite nanowire array retina. Nature, 2020, 581(7808), 278

    [24]

    Boyle W S, Smith G E. Charge coupled semiconductor devices. Bell Syst Tech J, 1970, 49(4), 587

    [25]

    Theuwissen A J. Solid-state imaging with charge-coupled devices. Springer Science & Business Media, 2006

    [26]

    Taylor S A. CCD and CMOS imaging array technologies: technology review. UK: Xerox Research Centre Europe, 1998

    [27]

    Bigas M, Cabruja E, Forest J, et al. Review of CMOS image sensors. Microelectron J, 2006, 37(5), 433

    [28]

    Kim Y, Chortos A, Xu W, et al. A bioinspired flexible organic artificial afferent nerve. Science, 2018, 360(6392), 998

    [29]

    Lee G J, Choi C, Kim D H, et al. Bioinspired artificial eyes: Optic components, digital cameras, and visual prostheses. Adv Funct Mater, 2017, 28(24), 1705202

    [30]

    Song Y M, Xie Y, Malyarchuk V, et al. Digital cameras with designs inspired by the arthropod eye. Nature, 2013, 497(7447), 95

    [31]

    Jeong K H, Kim J, Lee L P. Biologically inspired artificial compound eyes. Science, 2006, 312(5773), 557

    [32]

    Ko H C, Stoykovich M P, Song J, et al. A hemispherical electronic eye camera based on compressible silicon optoelectronics. Nature, 2008, 454(7205), 748

    [33]

    Posch C. Bio-inspired vision. J Instru, 2012, 7(01), C01054

    [34]

    Gollisch T, Meister M. Eye smarter than scientists believed: Neural computations in circuits of the retina. Neuron, 2010, 65(2), 150

    [35]

    Masland R H. The fundamental plan of the retina. Nat Neurosci, 2001, 4(9), 877

    [36]

    Rodieck R W, Rodieck R W. The first steps in seeing. Sunderland, MA: Sinauer Associates, 1998

    [37]

    Cho D D, Lee T. A review of bioinspired vision sensors and their applications. Sens Mater, 2015, 27(6), 447

    [38]

    Pereda A E. Electrical synapses and their functional interactions with chemical synapses. Nat Rev Neurosci, 2014, 15(4), 250

    [39]

    Tan Z H, Yang R, Terabe K, et al. Synaptic metaplasticity realized in oxide memristive devices. Adv Mater, 2016, 28(2), 377

    [40]

    Yu H, Gong J, Wei H, et al. Mixed-halide perovskite for ultrasensitive two-terminal artificial synaptic devices. Mater Chem Front, 2019, 3(5), 941

    [41]

    Etienne-Cummings R, Van der Spiegel J. Neuromorphic vision sensors. Sens Actuators A, 1996, 56(1/2), 19

    [42]

    Indiveri G, Douglas R. Neuromorphic vision sensors. Science, 2000, 288(5469), 1189

    [43]

    Moini A. Vision chips. Springer Science & Business Media, 1999

    [44]

    Lichtsteiner P, Posch C, Delbruck T. A 128 × 128 120 db 30 mW asynchronous vision sensor that responds to relative intensity change. 2006 IEEE International Solid State Circuits Conference – Digest of Technical Papers, 2006, 2060

    [45]

    Lichtsteiner P, Posch C, Delbruck T. A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor. IEEE J Solid-State Circuits, 2008, 43(2), 566

    [46]

    Leñero-Bardallo J A, Serrano-Gotarredona T, Linares-Barranco B. A 3.6 μs latency asynchronous frame-free event-driven dynamic-vision-sensor. IEEE J Solid-State Circuits, 2011, 46(6), 1443

    [47]

    Posch C, Matolin D, Wohlgenannt R. A QVGA 143 dB dynamic range frame-free pwm image sensor with lossless pixel-level video compression and time-domain CDS. IEEE J Solid-State Circuits, 2011, 46(1), 259

    [48]

    Posch C, Matolin D, Wohlgenannt R. A QVGA 143dB dynamic range asynchronous address-event PWM dynamic image sensor with lossless pixel-level video compression. 2010 IEEE International Solid-State Circuits Conference (ISSCC), 2010, 400

    [49]

    Berner R, Brandli C, Yang M, et al. A 240 × 180 10 mW 12 μs latency sparse-output vision sensor for mobile applications. 2013 Symposium on VLSI Circuits, 2013, C186

    [50]

    Brandli C, Berner R, Yang M, et al. A 240 × 180 130 dB 3 μs latency global shutter spatiotemporal vision sensor. IEEE J Solid-State Circuits, 2014, 49(10), 2333

    [51]

    Lichtsteiner P, Delbruck T. A 64 × 64 AER logarithmic temporal derivative silicon retina. Research in Microelectronics and Electronics, 2005 PhD, 2005, 2, 202

    [52]

    Zhou F, Liu Y, Shen X, et al. Low-voltage, optoelectronic CH3NH3PbI3– xCl x memory with integrated sensing and logic operations. Adv Funct Mater, 2018, 28(15), 1800080

    [53]

    Lei S, Wen F, Li B, et al. Optoelectronic memory using two-dimensional materials. Nano Lett, 2015, 15(1), 259

    [54]

    Lee J, Pak S, Lee Y W, et al. Monolayer optical memory cells based on artificial trap-mediated charge storage and release. Nat Commu, 2017, 8(1), 14734

    [55]

    Lee D, Hwang E, Lee Y, et al. Multibit MoS2 photoelectronic memory with ultrahigh sensitivity. Adv Mater, 2016, 28(41), 9196

    [56]

    Lipatov A, Sharma P, Gruverman A, et al. Optoelectrical molybdenum disulfide (MoS2)-ferroelectric memories. ACS Nano, 2015, 9(8), 8089

    [57]

    Roy K, Padmanabhan M, Goswami S, et al. Graphene–MoS2 hybrid structures for multifunctional photoresponsive memory devices. Nat Nanotechnol, 2013, 8(11), 826

    [58]

    Xiang D, Liu T, Xu J, et al. Two-dimensional multibit optoelectronic memory with broadband spectrum distinction. Nat Commun, 2018, 9(1), 2966

    [59]

    Qin S, Wang F, Liu Y, et al. A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater, 2017, 4(3), 035022

    [60]

    Ni Z, Wang Y, Liu L, et al. Hybrid structure of silicon nanocrystals and 2D WSe2 for broadband optoelectronic synaptic devices. 2018 IEEE International Electron Devices Meeting (IEDM), 2018, 38.5.1

    [61]

    Wang W, Panin G N, Fu X, et al. MoS2 memristor with photoresistive switching. Sci Rep, 2016, 6(1), 31224

    [62]

    Tran M D, Kim H, Kim J S, et al. Two-terminal multibit optical memory via van der Waals heterostructure. Adv Mater, 2019, 31(7), 1807075

    [63]

    Hu L, Yuan J, Ren Y, et al. Phosphorene/ZnO nano-heterojunctions for broadband photonic nonvolatile memory applications. Adv Mater, 2018, 30(30), 1801232

    [64]

    Zhou F, Chen J, Tao X, et al. 2D materials based optoelectronic memory: Convergence of electronic memory and optical sensor. Research, 2019, 2019, 9490413

    [65]

    Chai Y. In-sensor computing for machine vision. Nature, 2020, 579, 32

    [66]

    Zhang K, Jung Y H, Mikael S, et al. Origami silicon optoelectronics for hemispherical electronic eye systems. Nat Commun, 2017, 8(1), 1782

    [67]

    Laughlin S B, van Steveninck R R R, Anderson J C. The metabolic cost of neural information. Nat Neurosci, 1998, 1(1), 36

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    F Y Liao, F C Zhou, Y Chai, Neuromorphic vision sensors: Principle, progress and perspectives[J]. J. Semicond., 2021, 42(1): 013105. doi: 10.1088/1674-4926/42/1/013105.

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    Manuscript received: 05 June 2020 Manuscript revised: 09 July 2020 Online: Accepted Manuscript: 08 September 2020 Uncorrected proof: 21 September 2020 Published: 09 January 2021

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