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SCIENTIA SINICA Informationis, Volume 50 , Issue 6 : 892-912(2020) https://doi.org/10.1360/SSI-2019-0248

Recent progress on optoelectronic synaptic devices

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  • ReceivedNov 18, 2019
  • AcceptedDec 19, 2019
  • PublishedMay 25, 2020

Abstract

Neuromorphic (brain-like) computing has great potential to solve the von Neumann bottleneck due to its self-adaptive learning, high-parallel computing capability, and low-power consumption. Realization of neuromorphic computing depends on the development of synaptic devices that mimic biological synapses. Initially, synaptic devices were electronic, and such devices face significant challenges relative to optimization of bandwidth, connectivity, and density. It has been recently shown that the incorporation of light to make optoelectronic synaptic devices brings new opportunities for the development of synaptic devices. On one hand, light enables high bandwidth, low crosstalk, low energy consumption, and no delay. On the other hand, optoelectronic devices can be used to simulate special neurobehavioral functions, such as vision. As the basis of optoelectronic integrated neural networks, optoelectronic synaptic devices are expected to greatly contribute to the development of high-performance and low-power neuromorphic computing. In this review we introduce the basic properties of optoelectronic synaptic devices. Different types and applications that have been reported for optoelectronic devices are discussed. In addition, future prospects for the development of optoelectronic synaptic devices is outlined.


Funded by

国家重点研发计划(2017YFA0205704,2018YFB2200101)

国家自然科学基金委员会重大研究计划培育项目(91964107)

国家自然科学基金委员会面上项目(61774133)

国家自然科学基金委员会创新研究群体项目(61721005)


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  • Figure 1

    (Color online) (a) Schematics of a biological synapse [38]@Copyright 2016 WILEY-VCH Verlag GmbH & Co. KGaA; (b) Schematics of artificial synaptic devices with 2 terminals or 3 terminals

  • Figure 2

    (Color online) (a) Dependence of the $\Delta$PSC amplitudes on gate voltage for a synaptic device based on graphene hybrid phototransistor; (b) dependence of $\Delta$IPSC and $\Delta$EPSC on spike duration for the graphene hybrid phototransistor [44]@copyright 2017 IOP Publishing Ltd.; (c) dependence of the EPSC on drain voltage for a optoelectronic neuromorphic device based on pn-junction-decorated oxide thin-film transistor [45]@Copyright 2019 Elsevier Ltd.; (d) dependence of the EPSC on photonic pulse intensity for an all-oxide-based highly transparent photonic synapse [46]@Copyright 2018 American Chemical Society; (e) dependence of EPSC and relaxation time on pulse width at different light intensities for a MoO$_{x}$optoelectronic resistive random access memory [47]@Copyright 2019 Springer Nature; (f) dependence of the EPSC on the duration time of the laser spikes with different wavelengths for the Si-NC/WSe$_{2}$synaptic device [48]@Copyright 2018 IEEE

  • Figure 3

    (Color online) (a) Paired-pulse facilitation at the granule cell to Purkinje cell synapse [62]@Copyright 1996 Society for Neuroscience; (b) the PPF behavior of a graphene hybrid phototransistor [63]@Copyright 2016 Optical Society of America; (c) PPF behaviors of an InAs nanowire phototransistor [64]@Copyright 2018 IOP Publishing Ltd.; (d) dependence of the PPF index on pulse intervals for the laser spikes with different gate biases [65]@Copyright 2018 IEEE; (e) dependence of the PPF index on pulse intervals for the laser spikes with different light pulse intensities; (f) dependence of the PPF index on pulse intervals for the laser spikes with different wavelengths [66]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 4

    Schematic illustration of a typical memory model in psychology [70]@Copyright 2017 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 5

    (Color online) Channel conductance change ($\Delta~G$ as a function of (a) presynaptic light pulse number, protectłinebreak (b) presynaptic light pulse intensity, and (c) presynaptic light pulse width [76]@Copyright 2019 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 6

    (Color online) (a) STDP measurements in hippocampal glutamatergic synapses [39]@Copyright 1998 Society for Neuroscience; (b) four forms of STDP [89]@Copyright 2010 Shouval, Wang and Wittenberg

  • Figure 7

    (Color online) STDP learning behaviors mimicked on a deep-ultraviolet-triggered InZnO phototransistors [93]@Copyright 2018 AIP Publishing. (a) and (b) The asymmetric Hebbian learning rule; (c) and (d) the symmetric Hebbian learning rule and the waveform of optical/electrical spike for achieving them

  • Figure 8

    (Color online) (a) Biological experience-dependent plasticity in the visual cortex [96]@Copyright 1996 Springer Nature; (b) EPSCs recorded in response to the stimulus train with different frequencies; (c) ${\Delta}S$ plotted as a function of presynaptic spike frequency [97]@Copyright 2019 The Royal Society of Chemistry; (d) ${\Delta}S$ after 50 light pulses plotted as a function of gate voltage [65]@Copyright 2018 IEEE

  • Figure 9

    (Color online) Schematic of calculation methods for energy consumption of single synaptic event in optoelectronic synaptic devices. (a) The 1st method; (b) the 2nd method; (c) the 3rd method

  • Figure 10

    (Color online) (a) Energy-band diagrams of an IGZO-based photonic neuromorphic device in dark conditions and under illumination; (b) the relationship between the activation energies ($E_{\rm~a}$) and the relaxation time constant for various AOSs [70]@Copyright 2017 WILEY-VCH Verlag GmbH & Co. KGaA; (c) the photonic operation concept of an IGZO-based TFT device; (d) illustration of atomic structures for N$\rm_R$ In$\rm_H$ and Ga$\rm_H$IGZO films [106]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA; (e) schematic energy-band diagrams for the formation of ${{\rm~V}_{\rm~O}}^{2+}$and ${{\rm~V}_{\rm~O}}^{2+}$/${\rm~O}_{\rm~i}$; (f) schematic energy vs. lattice relaxation curves of the (0) and (2+) charge state in $\alpha$- and $\beta$-type configurations (the dashed and solid arrowline denotes the ionization and recovery process, respectively);protectłinebreak (g) the EPSC of IGZO, SnO$_x$/IGZO and PP/SnO$_x$/IGZO devices after light stimulation [45]@Copyright 2019 Elsevier Ltd.

  • Figure 11

    (Color online) (a) Schematic operation mechanism of an artificial optoelectronicsynapse based on ITO/Nb:SrTiO$_3$heterojunction; (b) mimicry of human visual memory [107]@Copyright 2019 American Chemical Society

  • Figure 12

    (Color online) (a) The mechanism responsible for the synaptic behavior of the CdS/MWCNT-based device [75]@Copyright 2016 WILEY-VCH Verlag GmbH & Co. KGaA; (b) schematic of a printed SWCNT phototransistor device under illumination of different light wavelengths; (c) low-pass filtering characteristics of the printed SWCNT transistors device [108]@Copyright 2019 American Chemical Society; (d) energy band diagram of excitation, thermalization, recombination, and trapping processes in the InAs nanowire phototransistor [64]@Copyright 2018 IOP Publishing Ltd.; (e) schematic of an array of Si-NC-based synaptic devices; (f) the STS curve, resulting for the Si-NC film measured at 77 K, has been shifted so that the Fermi level is at 0 V; (g) schematic model for the electronic structure and carrier behavior of Si NCs [100]@Copyright 2018 Elsevier Ltd.; (h) schematic of the Si-NC/WSe$_2$synaptic device structure [48]@Copyright 2018 IEEE

  • Figure 13

    (Color online) Neuromorphic computing simulation for image recognition. (a) Schematic of a taste aversion learning process for the treatment of alcoholism; (b) implementation of taste aversion learning with a synaptic Si-NC phototransistor; (c) example images in the MNIST database after the binarization with a pixel threshold of 50; (d) the architecture of the spiking neural network; (e) receptive fields of all output neurons in the network trained with the L/E$^+$STDP model; (f) receptive fields of all output neurons in the network trained with the E$^-$/E$^+$STDP model [101]@Copyright 2019 Elsevier Ltd.

  • Figure 14

    (Color online) (a) Mechanism of IGZO-based synaptic transistor under UV-light stimulation [109]@Copyright 2016 AIP Publishing; (b) schematic illustrations of the band diagram of the ZnO$_{1-x}$/AlO$_{y}$heterojunction [110]@Copyright 2018 American Chemical Society; (c) schematic diagram of a light-stimulated C8-BTBT synaptic transistors array; protectłinebreak (d) fabrication process of the T-shape transistors array; (e) dynamic learning and forgetting process of the T-shape synaptic transistors array [111]@Copyright 2018 American Chemical Society

  • Figure 15

    (Color online) (a) Mechanism of the IGZO-based EDL transistors [65]@Copyright 2018 IEEE; (b) mechanism of the MoS$_{2}$-based EDL transistors; (c) schematic of the photonic MoS$_{2}$synapse to function as both high-pass and low-pass photonic filters; (d) the spatiotemporal correlation effect; (e) the changes in synaptic weight ($\Delta~W$ as function of

  • Figure 16

    (Color online) Classical conditioning Pavlov's dog experiment emulated by MoS$_{2}$neuristors [112]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 17

    (Color online) (a) The time response of an NOR logic operation emulated by the graphene/SWCNTs hybrid phototransistor [44]@Copyright 2017 IOP Publishing Ltd.; (b) schematics of a two-terminal visible light transparent device based on ZnO/In$_{2}$O$_{3}$; (c) schematic illustrations of the working mechanism of the photonic synapse [46]@Copyright 2018 American Chemical Society; (d) endurance property of the light-programmed state and electric erased state of the photonic synapses based on inorganic perovskite quantum dots; (e) energy diagram of photonic synapses based on inorganic perovskite quantum dots during light programming operation and during electrical erasing operation under dark protectłinebreak condition [66]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA

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