Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware  

作　　者：Wenxiao Wang Yaqi Wang Feifei Yin Hongsen Niu Young-Kee Shin Yang Li Eun-Seong Kim Nam-Young Kim 

机构地区：[1]School of Information Science and Engineering,University of Jinan,Jinan 250022,People’s Republic of China [2]RFIC Centre,NDAC Centre,Kwangwoon University,Nowon-gu,Seoul 139-701,South Korea [3]Department of Electronics Engineering,Kwangwoon University,Nowon-Gu,Seoul 139-701,South Korea [4]School of Microelectronics,Shandong University,Jinan 250101,People’s Republic of China [5]Department of Molecular Medicine and Biopharmaceutical Sciences,Seoul National University,Seoul 08826,South Korea

出　　处：《Nano-Micro Letters》2024年第7期265-280,共16页纳微快报（英文版）

基　　金：This work was supported by the Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017);National Research Foundation of Korea(NRF)grant funded by the Korea government(MIST)(RS-2023-00302751);by the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grants 2018R1A6A1A03025242 and 2018R1D1A1A09083353;by Qilu Young Scholar Program of Shandong University.

摘　　要：Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

关 键 词：Artificial intelligence Classical conditioning Neuromorphic computing Artificial visual memory Optoelectronic memristors ZnO Quantum dots 

分 类 号：TN60[电子电信—电路与系统] TB383.1[一般工业技术—材料科学与工程]