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.
Authors
- Haidong Yuan (ORCID: https://orcid.org/0000-0002-6493-4481)
- Zhenhua Lin (ORCID: https://orcid.org/0000-0002-2965-1769)
- Dongsheng Cui (ORCID: https://orcid.org/0009-0001-8651-4373)
- Yunhong Zhang (ORCID: https://orcid.org/0000-0002-2899-5068)
- Yifei Wang (ORCID: https://orcid.org/0000-0003-1354-7139)
- Yun Li (ORCID: https://orcid.org/0000-0003-1753-7317)
- Yue Hao (ORCID: https://orcid.org/0000-0001-7876-8878)
- Jingjing Chang (ORCID: https://orcid.org/0000-0003-3773-182X)
- Xiangxiang Gao (ORCID: https://orcid.org/0000-0003-4402-9764)
- Mengjiao Pei
- Jincheng Zhang
Institutions
- Xidian University (CN)
- Collaborative Innovation Center of Advanced Microstructures (CN)
- Nanjing University (CN)
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
Funders
- National Natural Science Foundation of China
- Key Technologies Research and Development Program