Coexistence of unipolar and bipolar resistive switching in optical synaptic memristors and neuromorphic computing

The human brain possesses a highly developed capability for sensing-memory-computing, and the integration of hardware with brain-like functions represents a novel approach to overcoming the von Neumann bottleneck. In this study, Ga 2 O 3 photoelectric memristors were successfully fabricated, enabling efficient visual information processing and complex recognition through the integration of optoelectronic synapses with digital storage. The memristors with a Pt/Ga 2 O 3 /Pt sandwich structure exhibit the coexistence of unipolar resistive switching (URS) and bipolar resistive switching (BRS), coupled with an impressive switching ratio and stable retention characteristics. The device demonstrates robust photo-responsive properties to ultraviolet (UV) light, which enables the realization of an array of 16 photoconductor types through the manipulation of four-timeframe pulse sequences. Exposure of the device to UV light elicits stable synaptic behaviors, including paired-pulse facilitation (PPF), short-term memory (STM), long-term memory (LTM), as well as learning-forgetting-relearning behavior. Moreover, the device exhibits outstanding image sensing, image memory, and neuromorphic visual pre-processing capabilities as a neuromorphic vision sensor (NVS). The integration of light pulse potentiation with electrical pulse depression yields a remarkable 100 conductances with superior linearity. This advanced functionality is further validated by the ability of the device to facilitate the recognition of 85.3% of handwritten digits by artificial neural networks (ANNs), which underscores the significant potential of artificial synapses in mimicking biological neural.

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Publication Details

Journal
Chip
Published
2024-12-27
DOI
https://doi.org/10.1016/j.chip.2024.100122
Citations
25
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
5.22

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article

Coexistence of unipolar and bipolar resistive switching in optical synaptic memristors and neuromorphic computing

Haidong Yuan, Zhenhua Lin, Dongsheng Cui, Yunhong Zhang et al.
25 citations
Chip
Advanced Memory and Neural Computing
5.22
article

Coexistence of unipolar and bipolar resistive switching in optical synaptic memristors and neuromorphic computing

Haidong Yuan, Zhenhua Lin, Dongsheng Cui, Yunhong Zhang, Yifei Wang, Yun Li, Yue Hao, Jingjing Chang, Xiangxiang Gao, Mengjiao Pei, Jincheng Zhang
article en
25 citations

Abstract

The human brain possesses a highly developed capability for sensing-memory-computing, and the integration of hardware with brain-like functions represents a novel approach to overcoming the von Neumann bottleneck. In this study, Ga 2 O 3 photoelectric memristors were successfully fabricated, enabling efficient visual information processing and complex recognition through the integration of optoelectronic synapses with digital storage. The memristors with a Pt/Ga 2 O 3 /Pt sandwich structure exhibit the coexistence of unipolar resistive switching (URS) and bipolar resistive switching (BRS), coupled with an impressive switching ratio and stable retention characteristics. The device demonstrates robust photo-responsive properties to ultraviolet (UV) light, which enables the realization of an array of 16 photoconductor types through the manipulation of four-timeframe pulse sequences. Exposure of the device to UV light elicits stable synaptic behaviors, including paired-pulse facilitation (PPF), short-term memory (STM), long-term memory (LTM), as well as learning-forgetting-relearning behavior. Moreover, the device exhibits outstanding image sensing, image memory, and neuromorphic visual pre-processing capabilities as a neuromorphic vision sensor (NVS). The integration of light pulse potentiation with electrical pulse depression yields a remarkable 100 conductances with superior linearity. This advanced functionality is further validated by the ability of the device to facilitate the recognition of 85.3% of handwritten digits by artificial neural networks (ANNs), which underscores the significant potential of artificial synapses in mimicking biological neural.

ChipVol. 4(1)
Xidian University (CN), Collaborative Innovation Center of Advanced Microstructures (CN), Nanjing University (CN)
National Natural Science Foundation of China, Key Technologies Research and Development Program
Affordable and clean energy
Openalex Percentile: Top 4%
Advanced Memory and Neural Computing
5.22
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