Optoelectronic neuromorphic devices and their applications

Shen Liu-Feng; Hu Ling-Xiang; Kang Feng-Wen; Ye Yu-Min; Zhuge Fei

doi:10.7498/aps.71.20220111

Abstract

Conventional computers based on the von Neumann architecture are inefficient in parallel computing and self-adaptive learning, and therefore cannot meet the rapid development of information technology that needs efficient and high-speed computing. Owing to the unique advantages such as high parallelism and ultralow power consumption, bioinspired neuromorphic computing can have the capability of breaking through the bottlenecks of conventional computers and is now considered as an ideal option to realize the next-generation artificial intelligence. As the hardware carriers that allow the implementing of neuromorphic computing, neuromorphic devices are very critical in building neuromorphic chips. Meanwhile, the development of human visual systems and optogenetics also provides a new insight into how to study neuromorphic devices. The emerging optoelectronic neuromorphic devices feature the unique advantages of photonics and electronics, showing great potential in the neuromorphic computing field and attracting more and more attention of the scientists. In view of these, the main purpose of this review is to disclose the recent research advances in optoelectronic neuromorphic devices and the prospects of their practical applications. We first review the artificial optoelectronic synapses and neurons, including device structural features, working mechanisms, and neuromorphic simulation functions. Then, we introduce the applications of optoelectronic neuromorphic devices particularly suitable for the fields including artificial vision systems, artificial perception systems, and neuromorphic computing. Finally, we summarize the challenges to the optoelectronic neuromorphic devices, which we are facing now, and present some perspectives about their development directions in the future.

Keywords:

Authors and contacts

1.
School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
2.
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
3.
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China

Corresponding author: Zhuge Fei, zhugefei@nimte.ac.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. U20A20209, 61874125), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB32050204), the Natural Science Foundation of Zhejiang Pvovince, China (Grant Nos. LD19E020001, LQ22F040003), and the Ningbo Natural Science Foundation, China (Grant No. 2021J139).

FullText HTML : translate this paragraph

References

[1]	Turing A M 1937 Proc. London Math. Soc. 42 230 Google Scholar
[2]	Von Neuman J 1993 IEEE Ann. Hist. Comput. 15 27 Google Scholar
[3]	Zidan M A, Strachan J P, Lu W D 2018 Nat. Electron. 1 22 Google Scholar
[4]	Attwell D, Laughlin S B 2001 Cereb. Blood Flow Metab. 21 1133 Google Scholar
[5]	Chen Z, Ríos C, Pernice W H P, Wrigh C D, Bhaskara H 2017 Sci. Adv. 3 e1700160 Google Scholar
[6]	Drachman D A 2005 Neurology 64 2004 Google Scholar
[7]	Indiveri G, Linares-Barranco B, Legenstein R, Deligeorgis G, Prodromakis T 2013 Nanotechnology 24 384010 Google Scholar
[8]	Indiveri G, Liu S C 2015 Proc. IEEE 103 1379 Google Scholar
[9]	Mead C 1990 Proc. IEEE 78 1629 Google Scholar
[10]	Merolla P A, Arthur J V, Alvarez-Icaza R, et al. 2014 Science 345 668 Google Scholar
[11]	Shen J C, Ma D, Gu Z H, Zhang M, Zhu X L, Xu X Q, Xu Q, Shen Y J, Pan G 2016 Sci. China Inf. Sci. 59 023401 Google Scholar
[12]	Jo S H, Chang T, Ebong I, Bhadviya B B, Mazumder P, Lu W 2010 Nano Lett. 10 1297 Google Scholar
[13]	Prezioso M, Merrikh-Bayat F, Hoskins B D, Adam G C, Likharev K K, Strukov D B 2015 Nature 521 61 Google Scholar
[14]	Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski J K, Aono M 2011 Nat. Mater. 10 591 Google Scholar
[15]	Han W, Tellez L A, Rangel M Jr, Motta S C, Zhang X, Perez I O, Canteras N S, Shammah-Lagnado S J, van den Pol A N, de Araujo I E 2017 Cell 168 311 Google Scholar
[16]	Seo S, Jo S H, Kim S, Shim J, Oh S, Kim J H, Heo K, Choi J W, Choi C, Oh S, Kuzum D, Wong H P, Park J H 2018 Nat. Commun. 9 5106 Google Scholar
[17]	Mennel L, Symonowicz J, Wachter S, Polyushkin D K, Molina-Mendoza A J, Mueller T 2020 Nature 579 62 Google Scholar
[18]	韩济生 2009 神经科学 (北京大学医学出版社) 第139—145页 Han J S 2009 Neuroscience (Beijing: Peking University Medical Press) pp139–145 (in Chinese)
[19]	Yonezu H, Miho A, Himeno T, Pak K, Takano Y 1989 Electron. Lett. 25 670 Google Scholar
[20]	Krishnamoorthy A V, Yayla G, Esener S C 1992 IEEE T. Neural Networ. 3 404 Google Scholar
[21]	Wen Z, Frahat N H, Lin S Y 1994 Opt. Lett. 19 1394 Google Scholar
[22]	Shainline J M, Buckley S M, McCaughan A N, Chiles J, Jafari-Salim A, Mirin R P, Nam S W 2018 J. Appl. Phys. 124 152130 Google Scholar
[23]	Han J K, Geum D M, Lee M W, Yu J M, Kim S K, Kim S, Choi Y K 2020 Nano Lett. 20 8781 Google Scholar
[24]	Bliss T V P, Collingridge G L 1993 Nature 361 31 Google Scholar
[25]	Zhuge X, Wang J R, Zhuge F 2019 Phys. Status Solidi-R 13 1900082 Google Scholar
[26]	Agnus G, Zhao W, Derycke V, Filoramo A, Lhuillier Y, Lenfant S, Vuillaume D, Gamrat C, Bourgoin J P 2010 Adv. Mater. 22 702 Google Scholar
[27]	Hu D C, Yang R, Jiang L, Guo X 2018 ACS Appl. Mater. Interfaces 10 6463 Google Scholar
[28]	Zhu X, Lu W D 2018 ACS Nano 12 1242 Google Scholar
[29]	Ham S, Choi S, Cho H, Na S I, Wang G 2019 Adv. Funct. Mater. 29 1806646 Google Scholar
[30]	Hu L X, Yang J, Wang J R, Cheng P H, Chua L O, Zhuge F 2020 Adv. Funct. Mater. 31 2005582 Google Scholar
[31]	Wang Y, Yang J, Ye W B, She D H, Chen J R, Lv Z Y, Roy V A L, Li H L, Zhou K, Yang Q, Zhou Y, Han S T 2019 Adv. Electron. Mater. 6 1900765 Google Scholar
[32]	Pradhan B, Das S, Li J, Chowdhury F, Cherusseri J, Pandey D, Dev D, Krishnaprasad A, Barrios E, Towers A, Gesquiere A, Tetard L, Roy T, Thomas J 2020 Sci. Adv. 6 eaay5225 Google Scholar
[33]	Ahmed T, Tahir M, Low M X, Ren Y, Tawfik S A, Mayes E L H, Kuriakose S, Nawaz S, Spencer M J S, Chen H, Bhaskaran M, Sriram S, Walia S 2021 Adv. Mater. 33 e2004207 Google Scholar
[34]	Hou Y X, Li Y, Zhang Z C, Li J Q, Qi D H, Chen X D, Wang J J, Yao B W, Yu M X, Lu T B, Zhang J 2021 ACS Nano 15 1497 Google Scholar
[35]	Feldmann J, Youngblood N, Wright C D, Bhaskaran H, Pernice W H P 2019 Nature 569 208 Google Scholar
[36]	Luo Z D, Xia X, Yang M M, Wilson N R, Gruverman A, Alexe M 2020 ACS Nano 14 746 Google Scholar
[37]	Guo F, Song M L, Wong M C, Ding R, Io W F, Pang S Y, Jie W J, Hao J H 2021 Adv. Funct. Mater. 32 2108014 Google Scholar
[38]	Wang G Z, Wang R B, Kong W Z, Zhang J H 2018 Cogn. Neurodyn. 12 615 Google Scholar
[39]	Yu J R, Yang X X, Gao G Y, Xiong Y, Wang Y F, Han J, Chen Y H, Zhang H, Sun Q J, Wang Z L 2021 Sci. Adv. 7 eabd9117 Google Scholar
[40]	Psaltis D, Lin S 1991 Proc. SPIE 1562 204 Google Scholar
[41]	Wu Q T, Dang B J, Lu C Y, Xu G W, Yang G H, Wang J W, Chuai X C, Lu N D, Geng D, Wang H, Li L 2020 Nano Lett. 20 8015 Google Scholar
[42]	Tan H W, Zhou Y F, Tao Q Z, Rosen J, van Dijken S 2021 Nat. Commun. 12 1120 Google Scholar
[43]	Tsai M Y, Lee K C, Lin C Y, Chang Y M, Watanabe K, Taniguchi T, Ho C H, Lien C H, Chiu P W, Lin Y F 2021 Adv. Funct. Mater. 31 2105345 Google Scholar
[44]	尼克尔斯 J G, 马丁 A R, 福克斯 P A, 布朗 D A, 戴蒙特 M E, 韦斯勃拉脱 D A 著 (杨雄里译) 2014 从神经元到脑: 第5版 (北京: 科学出版社) 第10页 Nicholls J G, Martin A R, Fuchs P A, Brown D A, Diamond M E, Weisblat D A (translated by Yang X L) 2014 From Neuron to Brain, Fifth Edition (Beijing: Science Press) p10 (in Chinese)
[45]	Kandel E R, Squire L R 2000 Science 290 1113 Google Scholar
[46]	Bi G, Poo M 1998 J. Neurosic. 18 10464 Google Scholar
[47]	Lee G, Baek J H, Ren F, Pearton S J, Lee G H, Kim J 2021 Small 17 e2100640 Google Scholar
[48]	Fioravante D, Regehr W G 2011 Curr. Opin. Neurobiol. 21 269 Google Scholar
[49]	Abbott L F, Regehr W G 2004 Nature 431 796 Google Scholar
[50]	Hennig M H 2013 Front Comput. Neurosci. 7 154 Google Scholar
[51]	Montgomery J M, Madison D V 2004 Trends Neurosci. 27 744 Google Scholar
[52]	Rachmuth G, Shouval H Z, Bear M F, Poon C S 2011 Proc. Natl. Acad. Sci. U. S. A. 108 E1266 Google Scholar
[53]	Zhao Y H, Liu B, Yang J L, He J, Jiang J 2020 Chin. Phys. Lett. 37 088501 Google Scholar
[54]	任宽, 张珂嘉, 秦溪子, 任焕鑫, 朱守辉, 杨峰, 孙柏, 赵勇, 张勇 2021 物理学报 70 078701 Google Scholar Ren K, Zhang K J, Qin X Z, Ren H X, Zhu S H, Yang F, Sun B, Zhao Y, Zhang Y 2021 Acta Phys. Sin. 70 078701 Google Scholar
[55]	Chua L 1971 IEEE Trans. Circuit Theory 18 507 Google Scholar
[56]	Kumar M, Abbas S, Kim J 2018 ACS Appl. Mater. Interfaces 10 34370 Google Scholar
[57]	Kumar M, Ban D K, Kim S M, Kim J, Wong C P 2019 Adv. Electron. Mater. 5 1900467 Google Scholar
[58]	Wang W X, Gao S, Li Y, Yue W J, Kan H, Zhang C W, Lou Z, Wang L L, Shen G Z 2021 Adv. Funct. Mater. 31 2101201 Google Scholar
[59]	Zhao X N, Wang Z Q, Li W T, Sun S W, Xu H Y, Zhou P, Xu J Q, Lin Y, Liu Y C 2020 Adv. Funct. Mater. 30 1910151 Google Scholar
[60]	Wang Y, Yang J, Wang Z P, Chen J R, Yang Q, Lv Z Y, Zhou Y, Zhai Y B, Li Z X, Han S T 2019 Small 15 e1805431 Google Scholar
[61]	Zhou Y, Yew K S, Ang D S, Kawashima T, Bera M K, Zhang H Z, Bersuker G 2015 Appl. Phys. Lett. 107 072107 Google Scholar
[62]	Zhou Y, Liu D N, Wang J H, Cheng Z Q, Liu L, Yang N, Liu Y X, Xia T, Liu X Y, Zhang X, Ye C, Xu Z, Xiong W, Chu P K, Yu X F 2020 ACS Appl. Mater. Interfaces 12 25108 Google Scholar
[63]	Maier P, Hartmann F, Rebello Sousa Dias M, Emmerling M, Schneider C, Castelano L K, Kamp M, Marques G E, Lopez-Richard V, Worschech L, Höfling S 2016 Appl. Phys. Lett. 109 023501 Google Scholar
[64]	ShanX Y, Zhao C Y, Wang X N, Wang Z Q, Fu S C, Lin Y, Zeng T, Zhao X N, Xu H Y, Zhang X T, Liu Y C 2021 Adv. Sci. 9 2104632 Google Scholar
[65]	Li H L, Jiang X T, Ye W B, Zhang H, Zhou L, Zhang F, She D H, Zhou Y, Han S T 2019 Nano Energy 65 104000 Google Scholar
[66]	Diorio C, Hasler P, Minch A, Mead C A 1996 IEEE Trans. Electron Devices 43 1972 Google Scholar
[67]	Zhu L Q, Xiao H, Liu Y H, Wan C J, Shi Y, Wan Q 2015 Appl. Phys. Lett. 107 143502 Google Scholar
[68]	Zhai Y B, Zhou Y, Yang X Q, Wang F, Ye W B, Zhu X J, She D H, Lu W D, Han S T 2020 Nano Energy 67 104262 Google Scholar
[69]	Qian C, Oh S, Choi Y, Kim J H, Sun J, Huang H, Yang J, Gao Y, Park J H, Cho J H 2019 Nano Energy 66 104095 Google Scholar
[70]	Sun J, Oh S, Choi Y, Seo S, Oh M J, Lee M, Lee W B, Yoo P J, Cho J H, Park J H 2018 Adv. Funct. Mater. 28 1804397 Google Scholar
[71]	Yin L, Han C, Zhang Q T, Ni Z Y, Zhao S Y, Wang K, Li D S, Xu M S, Wu H Q, Pi X D, Yang D R 2019 Nano Energy 63 103859 Google Scholar
[72]	Wang J X, Chen Y, Kong L A, Fu Y, Gao Y L, Sun J 2018 Appl. Phys. Lett. 113 151101 Google Scholar
[73]	Yang C M, Chen T C, Verma D, Li L J, Liu B, Chang W H, Lai C S 2020 Adv. Funct. Mater. 30 2001598 Google Scholar
[74]	Yang X Y, Xiong Z Y, Chen Y J, Ren Y, Zhou L, Li H L, Zhou Y, Pan F, Han S T 2020 Nano Energy 78 105246 Google Scholar
[75]	Mcculloch W S, Pitts W 1990 Bull. Math. Biol. 52 99 Google Scholar
[76]	Hodgkin A L, Huxley A F 1990 Bull. Math. Biol. 52 25 Google Scholar
[77]	Shainline J M, Buckley S M, Mirin R P, Nam S W 2017 Phys. Rev. Applied 7 034013 Google Scholar
[78]	Kumar M, Kim H S, Kim J 2019 Adv. Mater. 31 e1900021 Google Scholar
[79]	Chen S, Lou Z, Chen D, Shen G Z 2018 Adv. Mater. 30 1705400 Google Scholar
[80]	Kwon S M, Cho S W, Kim M, Heo J S, Kim Y H, Park S K 2019 Adv. Mater. 31 e1906433 Google Scholar
[81]	Chen Q L, Zhang Y, Liu S Z, Han T T, Chen X H, Xu Y Q, Meng Z Q, Zhang G L, Zheng X J, Zhao J J, Cao G Z, Liu G 2020 Adv. Intell. Syst. 2 2000122 Google Scholar
[82]	Hong S, Choi S H, Park J, Yoo H, Oh J Y, Hwang E, Yoon D H, Kim S 2020 ACS Nano 14 9796 Google Scholar
[83]	Zhou F C, Zhou Z, Chen J W, Choy T H, Wang J L, Zhang N, Lin Z Y, Yu S M, Kang J F, Wong H P, Chai Y 2019 Nat. Nanotechnol. 14 776 Google Scholar
[84]	Qiu W J, Huang Y L, Kong L A, Chen Y, Liu W R, Wang Z, Sun J, Wan Q, Cho J H, Yang J L, Gao Y L 2020 Adv. Funct. Mater. 30 2002325 Google Scholar
[85]	Wu L D, Wang Z W, Wang B W, Chen Q Y, Bao L, Yu Z Z, Yang Y F, Ling Y T, Qin Y B, Tang K C, Cai Y M, Huang R 2021 Nanoscale 13 3483 Google Scholar
[86]	Wang C Y, Liang S J, Wang S, Wang P F, Li Z A, Wang Z R, Gao A Y, Pan C, Liu C, Liu J, Yang H F, Liu X W, Song W H, Wang C, Cheng B, Wang X M, Chen K J, Wang Z L, Watanabe K, Taniguchi T, Yang J J, Miao F 2020 Sci. Adv. 6 eaba6173 Google Scholar
[87]	Jang H, Liu C Y, Hinton H, Lee M H, Kim H, Seol M, Shin H J, Park S, Ham D 2020 Adv. Mater. 32 e2002431 Google Scholar
[88]	Tan H W, Tao Q Z, Pande I, Majumdar S, Liu F, Zhou Y F, Persson P O A, Rosen J, van Dijken S 2020 Nat. Commun. 11 1369 Google Scholar
[89]	Kim S, Roe D G, Choi Y Y, Woo H, Park J, Lee J I, Choi Y, Jo S B, Kang M S, Song Y J, Jeong S, Cho J H 2021 Sci. Adv. 7 eabe3996 Google Scholar
[90]	Karbalaei Akbari M, Zhuiykov S 2019 Nat. Commun. 10 3873 Google Scholar
[91]	Zhu Y B, Wu C X, Xu Z W, Liu Y, Hu H L, Guo T L, Kim T W, Chai Y, Li F S 2021 Nano Lett. 21 6087 Google Scholar
[92]	Wan C J, Cai P Q, Guo X T, Wang M, Matsuhisa N, Yang L, Lv Z S, Luo Y F, Loh X J, Chen X D 2020 Nat. Commun. 11 4602 Google Scholar
[93]	Yang X, Fang Y C, Yu Z Z, Wang Z W, Zhang T, Yin M H, Lin M, Yang Y C, Cai Y M, Huang R 2016 Nanoscale 8 18897 Google Scholar
[94]	Rankin C H, Abrams T, Barry R J, Bhatnagar S, Clayton D F, Colombo J, Coppola G, Geyer M A, Glanzman D L, Marsland S, McSweeney F K, Wilson D A, Wu C F, Thompson R F 2009 Neurobiol. Learn. Mem. 92 135 Google Scholar
[95]	He H K, Yang R, Zhou W, Huang H M, Xiong J, Gan L, Zhai T Y, Guo X 2018 Small 15 1800079 Google Scholar
[96]	Zhao B, Xiao M, Shen D Z, Zhou Y N 2020 Nanotechnology 31 125201 Google Scholar
[97]	Gong G D, Gao S, Xie Z L, Ye X Y, Lu Y, Yang H L, Zhu X J, Li R W 2021 Nanoscale 13 1029 Google Scholar
[98]	Akbari M K, Hu J, Verpoort F, Lu H L, Zhuiykov S 2020 Nano-Micro Lett. 12 83 Google Scholar
[99]	Zhou L, Zhang S R, Yang J Q, Miao J Y, Ren Y, Shan H Q, Xu Z X, Zhou Y, Han S T 2020 Nanoscale 12 1484 Google Scholar
[100]	Hawkins R D, Byrne J H 2015 Cold Spring Harb. Perspect. Biol. 7 a021709 Google Scholar
[101]	Liu L, Cheng Z Q, Jiang B, Liu Y X, Zhang Y L, Yang F, Wang J H, Yu X F, Chu P K, Ye C 2021 ACS Appl. Mater. Interfaces 13 30797 Google Scholar
[102]	Ahmed T, Kuriakose S, Mayes E L H, Ramanathan R, Bansal V, Bhaskaran M, Sriram S, Walia S 2019 Small 15 e1900966 Google Scholar
[103]	Feldman D E 2012 Neuron 75 556 Google Scholar
[104]	Abbott L F, Nelson S B 2000 Nat. Neurosci. 3 1178 Google Scholar
[105]	Caporale N, Dan Y 2008 Annu. Rev. Neurosci. 31 25 Google Scholar
[106]	Li Y, Zhong Y P, Zhang J J, Xu L, Wang Q, Sun H J, Tong H, Cheng X M, Miao X S 2014 Sci. Rep. 4 4906 Google Scholar

Cited By

图 1 光电神经形态器件的研究进展概述^{[16,23,29,33,39-43]}

Figure 1. Overview of advances in optoelectronic neuromorphic devices ^{[16,23,29,33,39-43]}.

DownLoad: Full-Size Img PowerPoint

图 2 (a) 生物神经元和突触结构示意图; (b) 生物神经元随不同神经递质信号产生的膜电位变化, 其中, 黑线、红线和蓝线分别表示神经元动作电位、兴奋性突触后电位和抑制性突触后电位^[47]

Figure 2. (a) Schematic illustration of the structure of biological neurons and synapses; (b) membrane potential of biological neurons with different neurotransmitter signals, where the black, red and blue curves denote the neuronal action potential, excitatory post-synaptic potential, and inhibitory post-synaptic potential respectively^[47].

DownLoad: Full-Size Img PowerPoint

图 3 基于忆阻器实现的光电协同型突触器件　(a) 基于ITO/ZnO_1–x/AlO_y/Al的忆阻器件结构示意图, 插图为器件横截面的透射电子显微镜 (TEM) 图像^[27]; (b) EPSC随刺激脉冲发生的光增强与电抑制过程^[27]; (c) 基于MAPbI₃的平面型忆阻器结构示意图; (d) 光照抑制碘空位形成和加速碘空位湮灭的过程^[28]; (e) 基于MAPbI₃的平面型忆阻器在黑暗与光照条件 (可见光, 1.29 μW/cm²) 下的LTP与LTD行为^[28]; (f) 基于MAPbI₃的垂直型忆阻器结构示意图^[29]; (g) 基于MAPbI₃的垂直型忆阻器在光照下内部工作机制示意图^[29]; (h) 基于MAPbI₃的垂直型忆阻器在光照与黑暗条件下的电增强与电抑制过程^[29]; (i) 基于InAs量子点的光电忆阻器结构示意图^[63]; (j) 基于InAs量子点的光电忆阻器电导在电压辅助下的光增强与光抑制过程^[63]

Figure 3. Optoelectronic cooperative synaptic devices based on memristor: (a) Structural illustration of memristive device based on ITO/ZnO_1–x/AlO_y/Al, and the corresponding transmission electron microscope (TEM) image^[27]; (b) photonic potentiation and electrical depression of stimulated pulses-dependent EPSC^[27]; (c) structural illustration of planar memristor based on MAPbI₃^[28]; (d) schematic illustration for illustrating how the light inhibits the formation (upper) and accelerates the annihilation (down) of iodine-related vacancies^[28]; (e) LTP and LTD behaviors of planar memristor based on MAPbI₃ under dark condition and upon illumination with a visible light at a power output of 1.29 μW/cm², respectively^[28]; (f) structural illustration of MAPbI₃-based vertical memristor^[29]; (g) schematic illustration of the working mechanism of MAPbI₃-based vertical memristor under light illumination^[29]; (h) dependence of electrical potentiation and depression of MAPbI₃-based vertical memristor on electrical pulses under dark and light illumination conditions^[29]; (i) structural illustration of InAs quantum dots (QDs)-based optoelectronic memristor^[63]; (j) photonic potentiation and depression of the conductance of InAs QDs-based optoelectronic memristor with the assistance of voltage^[63].

DownLoad: Full-Size Img PowerPoint

图 4 基于忆阻器实现的全光型突触器件　(a) 基于IGZO全光控忆阻器的工作模式^[30]; (b) 基于IGZO全光控忆阻器电导可逆调控特性及循环稳定性^[30]; (c) 基于IGZO全光控忆阻器的电导态保持特性, 分别通过光SET和光RESET获得^[30]; (d) 基于Ag-TiO₂纳米复合材料的忆阻器在可见光刺激下产生的LTP行为^[64]; (e) 基于Ag-TiO₂纳米复合材料的忆阻器在紫外光刺激下产生的LTD行为^[64]

Figure 4. All-optical synaptic devices based on memristor: (a) Working mode of all-optically controlled memristor based on IGZO^[30]; (b) reversible regulation characteristics of conductance (upper) and cycle stability (down)^[30]; (c) retention characteristics of memconductance states after optical SET (upper) and optical RESET (down) operations^[30]; visible light-induced LTP (d) and UV light-induced LTD (e) of the Ag-TiO₂ nanocomposite-based memristor^[64].

DownLoad: Full-Size Img PowerPoint

图 5 光电协同型突触晶体管　(a) 基于MoSe₂/Bi₂Se₃光电晶体管的器件结构^[31]; (b) 在0.15 mW/cm² (i) 和1.65 mW/cm² (ii) 功率密度的光脉冲刺激下突触后电流的变化^[31]; (c) 基于MoSe₂/Bi₂Se₃光电晶体管电导的光增强与电抑制过程^[31]; (d) 基于Gr-PQDs的光电晶体管在黑暗与光照(440 nm)条件下的输出特性曲线, 插图为光电晶体管的示意图^[32]; (e) 基于Gr-PQDs光电晶体管在光激发 (i) 与光栅效应 (ii) 下的能级图, 其中VB和CB分别表示价带与导带^[32]; (f) 基于Gr-PQDs光电晶体管在光电协同作用下的LTP与LTD行为^[32]

Figure 5. Optoelectronic cooperative synaptic transistors: (a) Schematic illustration of the structure of MoSe₂/Bi₂Se₃-based phototransistor^[31]; (b) dependence of the change of post-synaptic current on the time after continuously stimulating with the photonic pulses at the light intensity of 0.15 mW/cm² (i) and 1.65 mW/cm² (ii)^[31]; (c) photonic potentiation and electrical depression of the conductance of MoSe₂/Bi₂Se₃-based phototransistor^[31]; (d) output characteristic curve of the Gr-PQDs-based phototransistor under dark condition and 440 nm light illustration, where the phototransistor structure, as seen in the inset, is also given here^[32]; (e) schematic illustration of the energy band diagram for Gr-PQDs-based phototransistor under consideration of photoexcitation (i) and photogating effect (ii), where the VB and CB denote valence band and conduction band, respectively^[32]; (f) LTP and LTD behaviors of Gr-PQDs-based phototransistor under optoelectronic cooperation^[32].

DownLoad: Full-Size Img PowerPoint

图 6 全光型突触晶体管　(a) BP基光电晶体管结构示意图^[33]; (b), (c) BP基光电晶体管在280 nm与365 nm波长光脉冲刺激下的光电响应^[33]; (d) BP基光电晶体管LTP与LTD突触行为模拟^[33]; (e) Pyr-GDY/Gr/PbS-QD基光电晶体管结构示意图^[34]; (f) Pyr-GDY/Gr/PbS-QD基光电晶体管在450 nm与980 nm波长光照射下的能带图^[34]; (g) Pyr-GDY/Gr/PbS-QD基光电晶体管LTP与LTD突触行为模拟^[34]

Figure 6. All-optically controlled synaptic transistors: (a) Schematic illustration of the structure of fully light-controlled optoelectronic transistor based on BP^[33]; (b), (c) optoelectronic response of BP-based optoelectronic transistor upon stimulation with 280 nm (b) and 365 nm (c) light pulses^[33]; (d) LTP and LTD behaviors of BP-based optoelectronic transistor upon stimulation with 280 nm and 365 nm light pulses^[33]; (e) schematic illustration of the structure of Pyr-GDY/Gr/PbS-QDs-based optoelectronic transistor^[34]; (f) mechanistic illustration for the bandgap change of Pyr-GDY/Gr/PbS-QD-based optoelectronic transistor upon illumination with the light wavelengths of 450 nm (left) and 980 nm (right)^[34]; (g) LTP and LTD behaviors of the Pyr-GDY/Gr/PbS-QD-based optoelectronic transistor^[34].

DownLoad: Full-Size Img PowerPoint

图 7 (a) Bi₂O₂Se/Gr基突触结构图^[72]; (b) Bi₂O₂Se/Gr基突触在红光 (i) 和紫外光 (ii) 照射下的能带图^[72]; (c) Bi₂O₂Se/Gr基突触在红光和紫外光照射下的突触后电流^[72]; (d) Bi₂O₂Se/Gr基突触在同样的红光和紫光光脉冲下实现突触LTP与LTD行为模拟^[72]; (e) Gr/MoS₂基突触结构图, 其中插图为光电晶体管的扫描电子显微镜 (SEM) 图像^[39]; (f) Gr/MoS₂基突触与TENG分离状态 (i) 与接触状态 (ii) 的工作原理及相应的能带图^[39]; (g) V_TENG随位移变化曲线, 其中插图为V_TENG输出的等效电路图^[39]; (h) Gr/MoS₂基的突触在光脉冲与TENG位移脉冲共同作用下实现的电流增加与降低过程^[39]

Figure 7. (a) Structural illustration of synaptic device based on Bi₂O₂Se/Gr heterojunction^[72]; (b) mechanism illustration for the bandgap change of this Bi₂O₂Se/Gr-based synaptic device upon illumination with red (i) and UV light (ii), along with the corresponding post-synaptic current (c) as well as LTP and LTD behaviors (d) stimulated by the same red and UV light ^[72]; (e) schematic illustration of the structure of artificial synapse based on Gr/MoS₂ heterostructure and the scanning electron microscope (SEM) image of a phototransistor (inset)^[39]; (f) working mechanistic principle and the corresponding bandgap illustration for this artificial synapse based on Gr/MoS₂ heterostructure at (i) separation state and (ii) contact state with TENG^[39]; (g) dependence of the variation of V_TENG value on the displacement, together with the equivalent circuit illustration for V_TENG output (inset)^[39]; (h) current depression and potentiation of the artificial synapse based on Gr/MoS₂ heterojunction^[39].

DownLoad: Full-Size Img PowerPoint

图 8 基于砷化镓的光电神经元结构图^[40]

Figure 8. Schematic illustration of the structure of photoelectric neuron based on GaAs^[40].

DownLoad: Full-Size Img PowerPoint

图 9 光电神经元器件　(a) 硅基光电神经元的TEM图^[23]; (b) 神经元器件在加光与撤光条件下的光电响应^[23]; (c) 神经元器件在不同功率光照射下的光电响应^[23]; (d) 基于 IGZO₄紫外传感器和NbO_x振荡器的人工视觉神经元结构示意图^[41]; (e) 人工视觉神经元在不同光照下的工作模式示意图^[41]; (f) 人工视觉神经元在黑暗和不同波长紫外光照射件下的4种发射行为^[41]

Figure 9. Optoelectronic neuron devices: (a) TEM image of silicon-based optoelectronic neuron^[23]; (b) optoelectronic response of neuron under light ON and light OFF^[23]; (c) optoelectronic response of neuron upon stimulation with different light intensity^[23]; (d) structural illustration for artificial visual neuron composed of IGZO₄-based UV sensor and NbO_x-based oscillator^[41]; (e) working mode of artificial visual neuron under different light illumination^[41]; (f) four different firing behaviors of artificial visual neuron in dark and upon stimulation with different wavelength UV light^[41].

DownLoad: Full-Size Img PowerPoint

图 10 光电神经形态器件在人工视觉系统中的应用　(a) 由In₂O₃基图像传感器与Al₂O₃基阻变存储器构建的人工视觉系统^[79]; (b) 具有明适应与暗适应功能的人工视觉系统^[80]; (c) 具有颜色识别功能的h-BN/WSe₂基光电突触器件^[16]; (d) MoO_x 基ORRAM结构示意图, 其中插图为器件横截面的SEM图^[83]; (e) 基于ORRAM阵列构建的人工视觉系统^[83]; (f) 基于二维WSe₂的光电二极管结构示意图^[17]; (g) 基于WSe₂光电二极管实现的分类器与自编码器应用^[17]

Figure 10. Optoelectronic neuromorphic devices for artificial vision system: (a) Artificial vision system integrated by image sensor based on In₂O₃ and resistive random access memory based on Al₂O₃^[79]; (b) artificial visual system having the functions of light and dark adaptation^[80]; (c) h-BN/WSe₂ heterojunction-based optoelectronic synaptic device with the function of color recognition^[16]; (d) schematic illustration for ORRAM structure based on Pd/MoO_x/ITO, in which the inset shows the SEM image of the cross section of the device^[83]; (e) artificial vision system constructed by ORRAM array^[83]; (f) schematic illustration of photodiode based on two-dimensional (2 D) WSe₂ materials^[17]; (g) applications of 2D WSe₂-based photodiode for classifier and autoencoder^[17].

DownLoad: Full-Size Img PowerPoint

图 11 光电神经形态器件在人工感知系统中的应用　(a) 由压力传感器与光电突触组成的人工神经系统^[88]; (b) 由光电二极管、突触晶体管以及机械臂组成的控制系统^[89]

Figure 11. Optoelectronic neuromorphic devices for artificial sensing system: (a) Artificial system composed of pressure sensors and optoelectronic synapses^[88]; (b) control system composed of photodiodes, synaptic transistors and robotic arms^[89].

DownLoad: Full-Size Img PowerPoint

图 12 光电神经形态器件在非联想学习中的应用　(a) MoS₂基光电忆阻器模拟习惯化与敏化行为^[95]; (b) ZnO基光电器件模拟敏化行为^[78]

Figure 12. Nonassociative learning based on optoelectronic neuromorphic devices: (a) Simulation of habituation and sensitization behaviors using the MoS₂-based optoelectronic memristor^[95]; (b) simulation of sensitization behavior using the ZnO-based optoelectronic device^[78].

DownLoad: Full-Size Img PowerPoint

图 13 光电神经形态器件在联想学习模拟中的应用　(a) 光电协同刺激实现的巴普洛夫实验^[101]; (b) 全光刺激实现的巴普洛夫实验^[102]

Figure 13. Associative learning based on optoelectronic neuromorphic devices. Paplov's experiment realized by optoelectronic co-stimulation^[101] (a) and by all-optical stimulation^[102] (b).

DownLoad: Full-Size Img PowerPoint

图 14 光电神经形态器件在STDP学习规则模拟中的应用　(a) 基于ReSe₂/h-BN/Gr光电晶体管实现的四种STDP学习规则^[43]; (b) 基于全光控忆阻器实现的STDP学习规则^[30]

Figure 14. STDP learning rules based on optoelectronic neuromorphic devices: (a) Four STDP learning rules based on ReSe2/h-BN/Gr phototransistors^[43]; (b) STDP learning rules based on all-optically controlled memristor^[30].

DownLoad: Full-Size Img PowerPoint

[1]	Turing A M 1937 Proc. London Math. Soc. 42 230 Google Scholar
[2]	Von Neuman J 1993 IEEE Ann. Hist. Comput. 15 27 Google Scholar
[3]	Zidan M A, Strachan J P, Lu W D 2018 Nat. Electron. 1 22 Google Scholar
[4]	Attwell D, Laughlin S B 2001 Cereb. Blood Flow Metab. 21 1133 Google Scholar
[5]	Chen Z, Ríos C, Pernice W H P, Wrigh C D, Bhaskara H 2017 Sci. Adv. 3 e1700160 Google Scholar
[6]	Drachman D A 2005 Neurology 64 2004 Google Scholar
[7]	Indiveri G, Linares-Barranco B, Legenstein R, Deligeorgis G, Prodromakis T 2013 Nanotechnology 24 384010 Google Scholar
[8]	Indiveri G, Liu S C 2015 Proc. IEEE 103 1379 Google Scholar
[9]	Mead C 1990 Proc. IEEE 78 1629 Google Scholar
[10]	Merolla P A, Arthur J V, Alvarez-Icaza R, et al. 2014 Science 345 668 Google Scholar
[11]	Shen J C, Ma D, Gu Z H, Zhang M, Zhu X L, Xu X Q, Xu Q, Shen Y J, Pan G 2016 Sci. China Inf. Sci. 59 023401 Google Scholar
[12]	Jo S H, Chang T, Ebong I, Bhadviya B B, Mazumder P, Lu W 2010 Nano Lett. 10 1297 Google Scholar
[13]	Prezioso M, Merrikh-Bayat F, Hoskins B D, Adam G C, Likharev K K, Strukov D B 2015 Nature 521 61 Google Scholar
[14]	Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski J K, Aono M 2011 Nat. Mater. 10 591 Google Scholar
[15]	Han W, Tellez L A, Rangel M Jr, Motta S C, Zhang X, Perez I O, Canteras N S, Shammah-Lagnado S J, van den Pol A N, de Araujo I E 2017 Cell 168 311 Google Scholar
[16]	Seo S, Jo S H, Kim S, Shim J, Oh S, Kim J H, Heo K, Choi J W, Choi C, Oh S, Kuzum D, Wong H P, Park J H 2018 Nat. Commun. 9 5106 Google Scholar
[17]	Mennel L, Symonowicz J, Wachter S, Polyushkin D K, Molina-Mendoza A J, Mueller T 2020 Nature 579 62 Google Scholar
[18]	韩济生 2009 神经科学 (北京大学医学出版社) 第139—145页 Han J S 2009 Neuroscience (Beijing: Peking University Medical Press) pp139–145 (in Chinese)
[19]	Yonezu H, Miho A, Himeno T, Pak K, Takano Y 1989 Electron. Lett. 25 670 Google Scholar
[20]	Krishnamoorthy A V, Yayla G, Esener S C 1992 IEEE T. Neural Networ. 3 404 Google Scholar
[21]	Wen Z, Frahat N H, Lin S Y 1994 Opt. Lett. 19 1394 Google Scholar
[22]	Shainline J M, Buckley S M, McCaughan A N, Chiles J, Jafari-Salim A, Mirin R P, Nam S W 2018 J. Appl. Phys. 124 152130 Google Scholar
[23]	Han J K, Geum D M, Lee M W, Yu J M, Kim S K, Kim S, Choi Y K 2020 Nano Lett. 20 8781 Google Scholar
[24]	Bliss T V P, Collingridge G L 1993 Nature 361 31 Google Scholar
[25]	Zhuge X, Wang J R, Zhuge F 2019 Phys. Status Solidi-R 13 1900082 Google Scholar
[26]	Agnus G, Zhao W, Derycke V, Filoramo A, Lhuillier Y, Lenfant S, Vuillaume D, Gamrat C, Bourgoin J P 2010 Adv. Mater. 22 702 Google Scholar
[27]	Hu D C, Yang R, Jiang L, Guo X 2018 ACS Appl. Mater. Interfaces 10 6463 Google Scholar
[28]	Zhu X, Lu W D 2018 ACS Nano 12 1242 Google Scholar
[29]	Ham S, Choi S, Cho H, Na S I, Wang G 2019 Adv. Funct. Mater. 29 1806646 Google Scholar
[30]	Hu L X, Yang J, Wang J R, Cheng P H, Chua L O, Zhuge F 2020 Adv. Funct. Mater. 31 2005582 Google Scholar
[31]	Wang Y, Yang J, Ye W B, She D H, Chen J R, Lv Z Y, Roy V A L, Li H L, Zhou K, Yang Q, Zhou Y, Han S T 2019 Adv. Electron. Mater. 6 1900765 Google Scholar
[32]	Pradhan B, Das S, Li J, Chowdhury F, Cherusseri J, Pandey D, Dev D, Krishnaprasad A, Barrios E, Towers A, Gesquiere A, Tetard L, Roy T, Thomas J 2020 Sci. Adv. 6 eaay5225 Google Scholar
[33]	Ahmed T, Tahir M, Low M X, Ren Y, Tawfik S A, Mayes E L H, Kuriakose S, Nawaz S, Spencer M J S, Chen H, Bhaskaran M, Sriram S, Walia S 2021 Adv. Mater. 33 e2004207 Google Scholar
[34]	Hou Y X, Li Y, Zhang Z C, Li J Q, Qi D H, Chen X D, Wang J J, Yao B W, Yu M X, Lu T B, Zhang J 2021 ACS Nano 15 1497 Google Scholar
[35]	Feldmann J, Youngblood N, Wright C D, Bhaskaran H, Pernice W H P 2019 Nature 569 208 Google Scholar
[36]	Luo Z D, Xia X, Yang M M, Wilson N R, Gruverman A, Alexe M 2020 ACS Nano 14 746 Google Scholar
[37]	Guo F, Song M L, Wong M C, Ding R, Io W F, Pang S Y, Jie W J, Hao J H 2021 Adv. Funct. Mater. 32 2108014 Google Scholar
[38]	Wang G Z, Wang R B, Kong W Z, Zhang J H 2018 Cogn. Neurodyn. 12 615 Google Scholar
[39]	Yu J R, Yang X X, Gao G Y, Xiong Y, Wang Y F, Han J, Chen Y H, Zhang H, Sun Q J, Wang Z L 2021 Sci. Adv. 7 eabd9117 Google Scholar
[40]	Psaltis D, Lin S 1991 Proc. SPIE 1562 204 Google Scholar
[41]	Wu Q T, Dang B J, Lu C Y, Xu G W, Yang G H, Wang J W, Chuai X C, Lu N D, Geng D, Wang H, Li L 2020 Nano Lett. 20 8015 Google Scholar
[42]	Tan H W, Zhou Y F, Tao Q Z, Rosen J, van Dijken S 2021 Nat. Commun. 12 1120 Google Scholar
[43]	Tsai M Y, Lee K C, Lin C Y, Chang Y M, Watanabe K, Taniguchi T, Ho C H, Lien C H, Chiu P W, Lin Y F 2021 Adv. Funct. Mater. 31 2105345 Google Scholar
[44]	尼克尔斯 J G, 马丁 A R, 福克斯 P A, 布朗 D A, 戴蒙特 M E, 韦斯勃拉脱 D A 著 (杨雄里译) 2014 从神经元到脑: 第5版 (北京: 科学出版社) 第10页 Nicholls J G, Martin A R, Fuchs P A, Brown D A, Diamond M E, Weisblat D A (translated by Yang X L) 2014 From Neuron to Brain, Fifth Edition (Beijing: Science Press) p10 (in Chinese)
[45]	Kandel E R, Squire L R 2000 Science 290 1113 Google Scholar
[46]	Bi G, Poo M 1998 J. Neurosic. 18 10464 Google Scholar
[47]	Lee G, Baek J H, Ren F, Pearton S J, Lee G H, Kim J 2021 Small 17 e2100640 Google Scholar
[48]	Fioravante D, Regehr W G 2011 Curr. Opin. Neurobiol. 21 269 Google Scholar
[49]	Abbott L F, Regehr W G 2004 Nature 431 796 Google Scholar
[50]	Hennig M H 2013 Front Comput. Neurosci. 7 154 Google Scholar
[51]	Montgomery J M, Madison D V 2004 Trends Neurosci. 27 744 Google Scholar
[52]	Rachmuth G, Shouval H Z, Bear M F, Poon C S 2011 Proc. Natl. Acad. Sci. U. S. A. 108 E1266 Google Scholar
[53]	Zhao Y H, Liu B, Yang J L, He J, Jiang J 2020 Chin. Phys. Lett. 37 088501 Google Scholar
[54]	任宽, 张珂嘉, 秦溪子, 任焕鑫, 朱守辉, 杨峰, 孙柏, 赵勇, 张勇 2021 物理学报 70 078701 Google Scholar Ren K, Zhang K J, Qin X Z, Ren H X, Zhu S H, Yang F, Sun B, Zhao Y, Zhang Y 2021 Acta Phys. Sin. 70 078701 Google Scholar
[55]	Chua L 1971 IEEE Trans. Circuit Theory 18 507 Google Scholar
[56]	Kumar M, Abbas S, Kim J 2018 ACS Appl. Mater. Interfaces 10 34370 Google Scholar
[57]	Kumar M, Ban D K, Kim S M, Kim J, Wong C P 2019 Adv. Electron. Mater. 5 1900467 Google Scholar
[58]	Wang W X, Gao S, Li Y, Yue W J, Kan H, Zhang C W, Lou Z, Wang L L, Shen G Z 2021 Adv. Funct. Mater. 31 2101201 Google Scholar
[59]	Zhao X N, Wang Z Q, Li W T, Sun S W, Xu H Y, Zhou P, Xu J Q, Lin Y, Liu Y C 2020 Adv. Funct. Mater. 30 1910151 Google Scholar
[60]	Wang Y, Yang J, Wang Z P, Chen J R, Yang Q, Lv Z Y, Zhou Y, Zhai Y B, Li Z X, Han S T 2019 Small 15 e1805431 Google Scholar
[61]	Zhou Y, Yew K S, Ang D S, Kawashima T, Bera M K, Zhang H Z, Bersuker G 2015 Appl. Phys. Lett. 107 072107 Google Scholar
[62]	Zhou Y, Liu D N, Wang J H, Cheng Z Q, Liu L, Yang N, Liu Y X, Xia T, Liu X Y, Zhang X, Ye C, Xu Z, Xiong W, Chu P K, Yu X F 2020 ACS Appl. Mater. Interfaces 12 25108 Google Scholar
[63]	Maier P, Hartmann F, Rebello Sousa Dias M, Emmerling M, Schneider C, Castelano L K, Kamp M, Marques G E, Lopez-Richard V, Worschech L, Höfling S 2016 Appl. Phys. Lett. 109 023501 Google Scholar
[64]	ShanX Y, Zhao C Y, Wang X N, Wang Z Q, Fu S C, Lin Y, Zeng T, Zhao X N, Xu H Y, Zhang X T, Liu Y C 2021 Adv. Sci. 9 2104632 Google Scholar
[65]	Li H L, Jiang X T, Ye W B, Zhang H, Zhou L, Zhang F, She D H, Zhou Y, Han S T 2019 Nano Energy 65 104000 Google Scholar
[66]	Diorio C, Hasler P, Minch A, Mead C A 1996 IEEE Trans. Electron Devices 43 1972 Google Scholar
[67]	Zhu L Q, Xiao H, Liu Y H, Wan C J, Shi Y, Wan Q 2015 Appl. Phys. Lett. 107 143502 Google Scholar
[68]	Zhai Y B, Zhou Y, Yang X Q, Wang F, Ye W B, Zhu X J, She D H, Lu W D, Han S T 2020 Nano Energy 67 104262 Google Scholar
[69]	Qian C, Oh S, Choi Y, Kim J H, Sun J, Huang H, Yang J, Gao Y, Park J H, Cho J H 2019 Nano Energy 66 104095 Google Scholar
[70]	Sun J, Oh S, Choi Y, Seo S, Oh M J, Lee M, Lee W B, Yoo P J, Cho J H, Park J H 2018 Adv. Funct. Mater. 28 1804397 Google Scholar
[71]	Yin L, Han C, Zhang Q T, Ni Z Y, Zhao S Y, Wang K, Li D S, Xu M S, Wu H Q, Pi X D, Yang D R 2019 Nano Energy 63 103859 Google Scholar
[72]	Wang J X, Chen Y, Kong L A, Fu Y, Gao Y L, Sun J 2018 Appl. Phys. Lett. 113 151101 Google Scholar
[73]	Yang C M, Chen T C, Verma D, Li L J, Liu B, Chang W H, Lai C S 2020 Adv. Funct. Mater. 30 2001598 Google Scholar
[74]	Yang X Y, Xiong Z Y, Chen Y J, Ren Y, Zhou L, Li H L, Zhou Y, Pan F, Han S T 2020 Nano Energy 78 105246 Google Scholar
[75]	Mcculloch W S, Pitts W 1990 Bull. Math. Biol. 52 99 Google Scholar
[76]	Hodgkin A L, Huxley A F 1990 Bull. Math. Biol. 52 25 Google Scholar
[77]	Shainline J M, Buckley S M, Mirin R P, Nam S W 2017 Phys. Rev. Applied 7 034013 Google Scholar
[78]	Kumar M, Kim H S, Kim J 2019 Adv. Mater. 31 e1900021 Google Scholar
[79]	Chen S, Lou Z, Chen D, Shen G Z 2018 Adv. Mater. 30 1705400 Google Scholar
[80]	Kwon S M, Cho S W, Kim M, Heo J S, Kim Y H, Park S K 2019 Adv. Mater. 31 e1906433 Google Scholar
[81]	Chen Q L, Zhang Y, Liu S Z, Han T T, Chen X H, Xu Y Q, Meng Z Q, Zhang G L, Zheng X J, Zhao J J, Cao G Z, Liu G 2020 Adv. Intell. Syst. 2 2000122 Google Scholar
[82]	Hong S, Choi S H, Park J, Yoo H, Oh J Y, Hwang E, Yoon D H, Kim S 2020 ACS Nano 14 9796 Google Scholar
[83]	Zhou F C, Zhou Z, Chen J W, Choy T H, Wang J L, Zhang N, Lin Z Y, Yu S M, Kang J F, Wong H P, Chai Y 2019 Nat. Nanotechnol. 14 776 Google Scholar
[84]	Qiu W J, Huang Y L, Kong L A, Chen Y, Liu W R, Wang Z, Sun J, Wan Q, Cho J H, Yang J L, Gao Y L 2020 Adv. Funct. Mater. 30 2002325 Google Scholar
[85]	Wu L D, Wang Z W, Wang B W, Chen Q Y, Bao L, Yu Z Z, Yang Y F, Ling Y T, Qin Y B, Tang K C, Cai Y M, Huang R 2021 Nanoscale 13 3483 Google Scholar
[86]	Wang C Y, Liang S J, Wang S, Wang P F, Li Z A, Wang Z R, Gao A Y, Pan C, Liu C, Liu J, Yang H F, Liu X W, Song W H, Wang C, Cheng B, Wang X M, Chen K J, Wang Z L, Watanabe K, Taniguchi T, Yang J J, Miao F 2020 Sci. Adv. 6 eaba6173 Google Scholar
[87]	Jang H, Liu C Y, Hinton H, Lee M H, Kim H, Seol M, Shin H J, Park S, Ham D 2020 Adv. Mater. 32 e2002431 Google Scholar
[88]	Tan H W, Tao Q Z, Pande I, Majumdar S, Liu F, Zhou Y F, Persson P O A, Rosen J, van Dijken S 2020 Nat. Commun. 11 1369 Google Scholar
[89]	Kim S, Roe D G, Choi Y Y, Woo H, Park J, Lee J I, Choi Y, Jo S B, Kang M S, Song Y J, Jeong S, Cho J H 2021 Sci. Adv. 7 eabe3996 Google Scholar
[90]	Karbalaei Akbari M, Zhuiykov S 2019 Nat. Commun. 10 3873 Google Scholar
[91]	Zhu Y B, Wu C X, Xu Z W, Liu Y, Hu H L, Guo T L, Kim T W, Chai Y, Li F S 2021 Nano Lett. 21 6087 Google Scholar
[92]	Wan C J, Cai P Q, Guo X T, Wang M, Matsuhisa N, Yang L, Lv Z S, Luo Y F, Loh X J, Chen X D 2020 Nat. Commun. 11 4602 Google Scholar
[93]	Yang X, Fang Y C, Yu Z Z, Wang Z W, Zhang T, Yin M H, Lin M, Yang Y C, Cai Y M, Huang R 2016 Nanoscale 8 18897 Google Scholar
[94]	Rankin C H, Abrams T, Barry R J, Bhatnagar S, Clayton D F, Colombo J, Coppola G, Geyer M A, Glanzman D L, Marsland S, McSweeney F K, Wilson D A, Wu C F, Thompson R F 2009 Neurobiol. Learn. Mem. 92 135 Google Scholar
[95]	He H K, Yang R, Zhou W, Huang H M, Xiong J, Gan L, Zhai T Y, Guo X 2018 Small 15 1800079 Google Scholar
[96]	Zhao B, Xiao M, Shen D Z, Zhou Y N 2020 Nanotechnology 31 125201 Google Scholar
[97]	Gong G D, Gao S, Xie Z L, Ye X Y, Lu Y, Yang H L, Zhu X J, Li R W 2021 Nanoscale 13 1029 Google Scholar
[98]	Akbari M K, Hu J, Verpoort F, Lu H L, Zhuiykov S 2020 Nano-Micro Lett. 12 83 Google Scholar
[99]	Zhou L, Zhang S R, Yang J Q, Miao J Y, Ren Y, Shan H Q, Xu Z X, Zhou Y, Han S T 2020 Nanoscale 12 1484 Google Scholar
[100]	Hawkins R D, Byrne J H 2015 Cold Spring Harb. Perspect. Biol. 7 a021709 Google Scholar
[101]	Liu L, Cheng Z Q, Jiang B, Liu Y X, Zhang Y L, Yang F, Wang J H, Yu X F, Chu P K, Ye C 2021 ACS Appl. Mater. Interfaces 13 30797 Google Scholar
[102]	Ahmed T, Kuriakose S, Mayes E L H, Ramanathan R, Bansal V, Bhaskaran M, Sriram S, Walia S 2019 Small 15 e1900966 Google Scholar
[103]	Feldman D E 2012 Neuron 75 556 Google Scholar
[104]	Abbott L F, Nelson S B 2000 Nat. Neurosci. 3 1178 Google Scholar
[105]	Caporale N, Dan Y 2008 Annu. Rev. Neurosci. 31 25 Google Scholar
[106]	Li Y, Zhong Y P, Zhang J J, Xu L, Wang Q, Sun H J, Tong H, Cheng X M, Miao X S 2014 Sci. Rep. 4 4906 Google Scholar

[1]	Jiang Zi-Han, Ke Shuo, Zhu Ying, Zhu Yi-Xin, Zhu Li, Wan Chang-Jin, Wan Qing. Flexible neuromorphic transistors and their biomimetric sensing application. Acta Physica Sinica, 2022, 71(14): 147301. doi: 10.7498/aps.71.20220308
[2]	Li Ce, Yang Dong-Liang, Sun Lin-Feng. Research progress of neuromorphic devices based on two-dimensional layered materials. Acta Physica Sinica, 2022, 71(21): 218504. doi: 10.7498/aps.71.20221424
[3]	Jiang Bi-Yi, Zhou Fei-Chi, Chai Yang. Application of neuromorphic resistive random access memory in image processing. Acta Physica Sinica, 2022, 71(14): 148504. doi: 10.7498/aps.71.20220463
[4]	Zhu Jia-Xue, Zhang Xu-Meng, Wang Rui, Liu Qi. Flexible memristive spiking neuron for neuromorphic sensing and computing. Acta Physica Sinica, 2022, 71(14): 148503. doi: 10.7498/aps.71.20212323
[5]	Wen Xin-Yu, Wang Ya-Sai, He Yu-Hui, Miao Xiang-Shui. Memristive brain-like computing. Acta Physica Sinica, 2022, 71(14): 140501. doi: 10.7498/aps.71.20220666
[6]	Shan Xuan-Yu, Wang Zhong-Qiang, Xie Jun, Zheng Jia-Hui, Xu Hai-Yang, Liu Yi-Chun. Recent progress in optoelectronic memristive devices for in-sensor computing. Acta Physica Sinica, 2022, 71(14): 148701. doi: 10.7498/aps.71.20220350
[7]	Chen Yang-Yang, He Yu-Hui, Miao Xiang-Shui, Yang Dao-Hong. 3D-NAND flash memory based neuromorphic computing. Acta Physica Sinica, 2022, 71(21): 210702. doi: 10.7498/aps.71.20220974
[8]	Ding Xue-Li, Gu Hua-Guang, Jia Bing, Li Yu-Ye. Anticipated synchronization of electrical activity induced by inhibitory autapse in coupled Morris-Lecar neuron model. Acta Physica Sinica, 2021, 70(21): 218701. doi: 10.7498/aps.70.20210912
[9]	Xie Ying, Zhu Zhi-Gang, Zhang Xiao-Feng, Ren Guo-Dong. Control of firing mode in nonlinear neuron circuit driven by photocurrent. Acta Physica Sinica, 2021, 70(21): 210502. doi: 10.7498/aps.70.20210676
[10]	Zhang Xiu-Fang, Ma Jun, Xu Ying, Ren Guo-Dong. Synchronization between FitzHugh-Nagumo neurons coupled with phototube. Acta Physica Sinica, 2021, 70(9): 090502. doi: 10.7498/aps.70.20201953
[11]	Ren Kuan, Zhang Ke-Jia, Qin Xi-Zi, Ren Huan-Xin, Zhu Shou-Hui, Yang Feng, Sun Bai, Zhao Yong, Zhang Yong. Research progress of neuromorphic computation based on memcapacitors. Acta Physica Sinica, 2021, 70(7): 078701. doi: 10.7498/aps.70.20201632
[12]	Xue Xiao-Dan, Wang Mei-Li, Shao Yu-Zhu, Wang Jun-Song. Neural firing rate homeostasis via inhibitory synaptic plasticity. Acta Physica Sinica, 2019, 68(7): 078701. doi: 10.7498/aps.68.20182234
[13]	Li Guo-Fang, Sun Xiao-Juan. Effects of hybrid synapses and partial time delay on stochastic resonance in a small-world neuronal network. Acta Physica Sinica, 2017, 66(24): 240501. doi: 10.7498/aps.66.240501
[14]	Yu Wen-Ting, Zhang Juan, Tang Jun. Effects of dynamic synapses, neuronal coupling, and time delay on firing of neuron. Acta Physica Sinica, 2017, 66(20): 200201. doi: 10.7498/aps.66.200201
[15]	Ren Guo-Dong, Wu Gang, Ma Jun, Chen Yang. Simulation of electric activity of neuron by setting up a reliable neuronal circuit driven by electric autapse. Acta Physica Sinica, 2015, 64(5): 058702. doi: 10.7498/aps.64.058702
[16]	Zhao Long, Yang Ji-Ping, Zheng Yan-Hong. Modulation of nonlinear coupling on the synchronization induced by linear coupling. Acta Physica Sinica, 2013, 62(2): 028701. doi: 10.7498/aps.62.028701
[17]	Wu Wang-Sheng, Tang Guo-Ning. Synchronizations of chaotic neuronal networks under different couplings. Acta Physica Sinica, 2012, 61(7): 070505. doi: 10.7498/aps.61.070505
[18]	Li Chun-Guang, Chen Jun. Chaos in a neuron model with adaptive feedback synapse: Electronic circuit design. Acta Physica Sinica, 2011, 60(5): 050503. doi: 10.7498/aps.60.050503
[19]	He Guo-Guang, Zhu Ping, Chen Hong-Ping, Xie Xiao-Ping. Synchronization in a threshold coupled chaotic neuronal map. Acta Physica Sinica, 2010, 59(8): 5307-5312. doi: 10.7498/aps.59.5307
[20]	Wang Chao-Qing, Xu Wei, Zhang Na-Min, Li Hai-Quan. Fitz hugh-nagumo neural system driven by colored noises. Acta Physica Sinica, 2008, 57(2): 749-755. doi: 10.7498/aps.57.749

Catalog

Vol. 71, Issue 14 - 2022-07-20

Metrics

Abstract views: 18099
PDF Downloads: 1386
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板