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

doi:10.1007/s40820-024-01338-z

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/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.

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 TiO₂ Nanowires: 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

Figures/Tables 5

Fig. 1 Conceptual design and structural characterizations. a Schematic of the proboscis extension response of a honeybee. b Schematic of the proposed ATZ-based device. c Cross-sectional SEM image of the TiO₂ NWs: ZnO QDs/FTO/glass structure. d EDS elemental mapping images of (I) Ti, (II) Zn, and (III) O. e Low magnification TEM image of the synthesized ZnO QDs. Inset: size distribution of 100 ZnO QDs. f High-resolution TEM of the ZnO QDs. Inset: crystalline structure of the ZnO QDs. g Selected area electron diffraction (SAED) image of the ZnO QDs. XPS spectrum of the TiO₂ NWs: ZnO QDs film: deconvoluted h Zn 2p and i Ti 2p spectra

Fig. 2 Electric-induced synaptic behavior. Consecutive I-V curves of the ATZ-based device for 15 cycles under a positive sweeping voltage (0 V → + 3 V → 0 V) and b negative sweeping voltage (0 V → − 3 V → 0 V). Current and voltage data vs. time of the ATZ-based device in the c positive and d negative regions. e PPF and f PPD index values (10 cycles) with respect to the interval of pulse pairs. g LTP/LTD properties of the ATZ-based device (+ 3 V for potentiation, − 3 V for depression). h Conductance variation of the ATZ-based device for different voltage pulse amplitudes. i Conductance variation of the ATZ-based device for different voltage durations and intervals

Fig. 3 Light-induced synaptic plasticity of the ATZ-based device. a Schematic of the light-induced performance measurement. b Current responses under different light densities (dark, 1.30, 2.76, 3.91, 6.40, 7.50, 9.64, 10.96, and 11.97 mW cm⁻²). c Current responses under different light durations (5, 10, 15, and 20 s). d Current response under consecutive light pulses. Inset: local zoomed view of d. e Light-induced learning-forgetting-relearning process of four cycles. f Light-induced PPF behavior (10 cycles). g LTP/LTD performance under the excitation of light, voltage, and light plus voltage. h LTP/LTD performance under the synergy of light and electrical modulation. i Schematic of the ANN-based neural network for MNIST dataset classification using LTP/LTD (light + voltage) properties of the device. j Normalized conductance of the LTP/LTD vs. the normalized number of pulses. k Variation in accuracy during 100 training epochs. l Confusion matrix for recognition of the numbers from 0 to 9

Fig. 4 Visual memory application of the ATZ-based device. a Schematic diagram of light testing mapped on a 3 × 7 memristive array (I) through a “W” shaped mask, and (II) a top view of the mask. The variation in conductance-mapped pattern (letter “W”) with decay time (0, 5, and 20 s) after b 2 s, c 5 s, and d 10 s of light radiation

Fig. 5 a Schematic of the classical PER of a honeybee. Light pulses emulate the CS (odor, no trigger response), and voltage pulses mimic the US (nectar, trigger response). b (I) Conductance response after excitement by CS (45 cycles of purely light pulses, 0.5 Hz) and (II) the decayed response within 200 ms after being excited by CS. (III) Conductance response after excitement by US (45 cycles of purely voltage pulses, 0.5 Hz) and (IV) the decayed response within 200 ms after being excited by US. Implement the four features in classical PER: c acquisition, d extinction, e recovery, and f generalization

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