Artificial optoelectronic synapse based on spatiotemporal irradiation to source-sharing circuitry of synaptic phototransistors

Abstract

To overcome the intrinsic inefficiency of the von Neumann architecture, neuromorphic devices that perform analog vector-matrix multiplication have been highlighted for achieving power- and time-efficient data processing. In particular, artificial synapses, of which conductance should be programmed to represent the synaptic weights of the artificial neural network, have been intensively researched to realize neuromorphic devices. Here, inspired by excitatory and inhibitory synapses, we develop an artificial optoelectronic synapse that shows both potentiation and depression characteristics triggered only by optical inputs. The design of the artificial optoelectronic synapse, in which excitatory and inhibitory synaptic phototransistors are serially connected, enables these characteristics by spatiotemporally irradiating the phototransistor channels with optical pulses. Furthermore, a negative synaptic weight can be realized without the need for electronic components such as comparators. With such attributes, the artificial optoelectronic synapse is demonstrated to classify three digits with a high recognition rate (98.3%) and perform image preprocessing via analog vector-matrix multiplication.

Keywords

amorphous oxide semiconductor / analog processing / artificial synapse / neuromorphic / optoelectronics

References

[1] von Neumann J. First draft of a report on the EDVAC. IEEE Ann Hist Comput. 1993;15(4):27-75.

[2] Kandel ER, Schwartz JH, Jessell TM, et al. Principles of Neural Science. Vol 4. McGraw-hill; 2000.

[3] Seung H, Choi C, Kim DC, et al. Integration of synaptic phototransistors and quantum dot light-emitting diodes for visualization and recognition of UV patterns. Sci Adv. 2022;8(41):eabq3101.

[4] Hu M, Strachan JP, Li ZY, et al. Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication. In: 2016 53rd ACM/EDAC/IEEE Design Automation Conf. (DAC); 2016:1-6.

[5] Kub FJ, Moon KK, Mack IA, Long FM. Programmable analog vector matrix multipliers. IEEE J Solid-State Circuits. 1990;25(1):207-214.

[6] Li YN, Song WH, Wang ZR, et al. Memristive field-programmable analog arrays for analog computing. Adv Mater. 2022;35:2206648.

[7] Ziegler T, Waser R, Wouters DJ, Menzel S. In-memory binary vector-matrix multiplication based on complementary resistive switches. Adv Intell Syst. 2020;2(10):2000134.

[8] Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski JK, Aono M. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater. 2011;10(8):591-595.

[9] Kwon SM, Kwak JY, Song S, et al. Large-area pixelized optoelectronic neuromorphic devices with multispectral light-modulated bidirectional synaptic circuits. Adv Mater. 2021;33(45):2105017.

[10] Sun Y, Li QJ, Zhu X, et al. In-sensor reservoir computing based on optoelectronic synapse. Adv Intell Syst. 2023;5(1):2200196.

[11] Sun L, Qu SD, Du Y, et al. Bio-inspired vision and neuromorphic image processing using printable metal oxide photonic synapses. ACS Photon. 2022;10(1):242-252.

[12] Sun LF, Wang ZR, Jiang J, et al. In-sensor reservoir computing for language learning via two-dimensional memristors. Sci Adv. 2021;7(20):eabg1455.

[13] Zhou FC, Zhou Z, Chen JW, et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat Nanotechnol. 2019;14(8):776-782.

[14] Khan S, Primavera BA, Chiles J, et al. Superconducting optoelectronic single-photon synapses. Nat Electron. 2022;5(10):650-659.

[15] Kim E, Hwang G, Kim D, et al. Orbital gating driven by Giant stark effect in tunneling phototransistors. Adv Mater. 2022;34(6):2106625.

[16] Wang Y, Yin L, Huang W, et al. Optoelectronic synaptic devices for neuromorphic computing. Adv Intell Syst. 2021;3(1):2000099.

[17] Wang XW, Wang BL, Zhang QH, et al. Grain-boundary engineering of monolayer MoS₂ for energy-efficient lateral synaptic devices. Adv Mater. 2021;33(32):2102435.

[18] Li Y, Wang JH, Yang Q, Shen GZ. Flexible artificial optoelectronic synapse based on lead-free metal halide nanocrystals for neuromorphic computing and color recognition. Adv Sci. 2022;9(22):2202123.

[19] Wang TY, Meng JL, Li QX, et al. Reconfigurable optoelectronic memristor for in-sensor computing applications. Nano Energy. 2021;89:106291.

[20] Jang H, Liu CY, Hinton H, et al. An atomically thin optoelectronic machine vision processor. Adv Mater. 2020;32:2002431.

[21] Lee D, Park M, Baek Y, Bae B, Heo J, Lee K. In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing. Nat Commun. 2022;13(1):5223.

[22] Seo S, Jo SH, Kim S, et al. Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat Commun. 2018;9(1):5106.

[23] Ahmed T, Tahir M, Low MX, et al. Fully light-controlled memory and neuromorphic computation in layered black phosphorus. Adv Mater. 2021;33(10):2004207.

[24] Yang CM, Chen TC, Verma D, et al. Bidirectional all-optical synapses based on a 2D Bi₂O₂Se/graphene hybrid structure for multifunctional optoelectronics. Adv Funct Mater. 2020;30(30):2001598.

[25] Ge SP, Huang FC, He JQ, et al. Bidirectional photoresponse in perovskite-ZnO heterostructure for fully optical-controlled artificial synapse. Adv Opt Mater. 2022;10(11):2200409.

[26] Ji RX, Feng GD, Jiang CL, et al. Fully light-modulated organic artificial synapse with the assistance of ferroelectric polarization. Adv Electron Mater. 2022;8(7):2101402.

[27] Alibart F, Zamanidoost E, Strukov DB. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun. 2013;4(1):2072.

[28] Wang C, Liang SJ, Wang CY, et al. Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array. Nat Nanotechnol. 2021;16(10):1079-1085.

[29] Kim H, Mahmoodi MR, Nili H, Strukov DB. 4K-memristor analog-grade passive crossbar circuit. Nat Commun. 2021;12(1):5198.

[30] Hess R, Murata K. Effects of glutamate and GABA on specific response properties of neurons in visual-cortex. Exp Brain Res. 1974;21(3):285-297.

[31] Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117(4):500-544.

[32] Li HK, Chen TP, Liu P, et al. A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnO_x-Al₂O₃ thin film structure. J Appl Phys. 2016;119(24):244505.

[33] Tan H, Ni ZY, Peng WB, et al. Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy. 2018;52:422-430.

[34] Shan XY, Zhao CY, Wang XN, et al. Plasmonic optoelectronic memristor enabling fully light-modulated synaptic plasticity for neuromorphic vision. Adv Sci. 2022;9(6):2104632.

[35] Chen PY, Peng XC, Yu SM. NeuroSim plus: an integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures. In: 2017 IEEE International Electron Devices Meeting (IEDM); 2017:6.1.1-6.1.4.

[36] Li C, Wang ZR, Rao MY, et al. Long short-term memory networks in memristor crossbar arrays. Nat Mach Intell. 2019;1(1):49-57.

[37] Nessler B, Pfeiffer M, Buesing L, Maass W. Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLoS Comput Biol. 2013;9(4):e1003037.

[38] Bradski G. The open CV library. Dobb's J. 2000;25(11):120-123.

[39] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825-2830.

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