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Vol. 16 (2024)

Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

https://doi.org/10.1007/s40820-024-01338-z
Wenxiao Wang
School of Information Science and Engineering, University of Jinan, Jinan 250022, People’s Republic of China
Yaqi Wang
School of Information Science and Engineering, University of Jinan, Jinan 250022, People’s Republic of China
Feifei Yin
RFIC Centre, NDAC Centre, Kwangwoon University, Nowon‑gu, Seoul 139‑701, South Korea
Hongsen Niu
RFIC Centre, NDAC Centre, Kwangwoon University, Nowon‑gu, Seoul 139‑701, South Korea
Young‑Kee Shin
Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 08826, South Korea
Yang Li
School of Information Science and Engineering, University of Jinan, Jinan 250022, People’s Republic of China
Eun‑Seong Kim
RFIC Centre, NDAC Centre, Kwangwoon University, Nowon‑gu, Seoul 139‑701, South Korea
Nam‑Young Kim
RFIC Centre, NDAC Centre, Kwangwoon University, Nowon‑gu, Seoul 139‑701, South Korea

Corresponding Author: Nam‑Young Kim

nykm@kw.ac.kr

Nano-Micro Letters, Vol. 16 (2024), Article Number: 133

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/TiO2 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. 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.


Highlights:
1 An optoelectronic memristor with bioinspired neuromorphic behavior was proposed based on Ag/TiO2 Nanowires: ZnO QDs/FTO.
2 The proposed device establishes superior long/short-term synaptic plasticity in response to electrical and light stimuli, and a neuromorphic computing task is effectively implemented based on its optoelectronic performance.
3 The device emulated complex biological associative learning behaviors, including the four features of acquisition, extinction, restoration, and generalization.

Keywords

Artificial intelligence Classical conditioning Neuromorphic computing Artificial visual memory Optoelectronic memristors ZnO Quantum dots
Wang, Wenxiao, Yaqi Wang, Feifei Yin, Hongsen Niu, Young‑Kee Shin, Yang Li, Eun‑Seong Kim, and Nam‑Young Kim. 2024. “Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware”. Nano-Micro Letters 16 (February):133. https://doi.org/10.1007/s40820-024-01338-z.
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