Tailoring Classical Conditioning Behavior in TiO<sub>2</sub> Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Abstract

Abstract:

Neuromorphic hardware equipped with associative learning 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 optoelectronic memristor based on Ag/TiO₂Nanowires: 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. Comprehensively, 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.

Key words:Artificial intelligence,Classical conditioning,Neuromorphic computing,Artificial visual memory,Optoelectronic memristors,ZnO Quantum dots

Wenxiao Wang, Yaqi Wang, Feifei Yin, Hongsen Niu, Young-Kee Shin, Yang Li, Eun-Seong Kim, Nam-Young Kim. Tailoring Classical Conditioning Behavior in TiO₂Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware[J]. Nano-Micro Letters, 2024, 16(1): 133-.

Wenxiao Wang, Yaqi Wang, Feifei Yin, Hongsen Niu, Young-Kee Shin, Yang Li, Eun-Seong Kim, Nam-Young Kim. [J]. Nano-Micro Letters, 2024, 16(1): 133-.

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URL:https://www.qk.sjtu.edu.cn/nml/EN/10.1007/s40820-024-01338-z

https://www.qk.sjtu.edu.cn/nml/EN/Y2024/V16/I1/133

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