Fig. 1.
(Color online) (a) Schematic illustration of the device structure and fabrication process flow. (b) Schematic diagram of the synaptic information transmission in neurons. (c) The X-ray photoelectron spectroscopy (XPS).
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| Citation: |
Jieru Song, Jialin Meng, Tianyu Wang, Changjin Wan, Hao Zhu, Qingqing Sun, David Wei Zhang, Lin Chen. InGaZnO-based photoelectric synaptic devices for neuromorphic computing[J]. Journal of Semiconductors, 2024, 45(9): 092402. doi: 10.1088/1674-4926/24040038
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J R Song, J L Meng, T Y Wang, C J Wan, H Zhu, Q Q Sun, D W Zhang, and L Chen, InGaZnO-based photoelectric synaptic devices for neuromorphic computing[J]. J. Semicond., 2024, 45(9), 092402 doi: 10.1088/1674-4926/24040038
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InGaZnO-based photoelectric synaptic devices for neuromorphic computing
DOI: 10.1088/1674-4926/24040038
More Information
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Jieru Song is currently a master's student at the School of Microelectronics, Fudan University, under the supervision of Prof. Lin Chen. She received her bachelor's degree in 2022 from the School of Electronic Science and Engineering, Nanjing University. Her research focuses on photoelectric memristors and neuromorphic computing -
Jialin Meng received her Ph.D degree in microelectronics and solid-state electronics from Fudan University, Shanghai, China in 2022. She is currently a Postdoctor in the School of Microelectronics, Fudan University. Her research focuses on the optoelectronic neuromorphic devices, in-sensor computing, and flexible electronics -
Lin Chen (February 1986) received the Ph.D degree in microelectronics and solid-state electronics from Fudan University, Shanghai, China in 2012. He is currently a Professor in the School of Microelectronics, Fudan University. His research interests include semiconductor devices, as well as advanced CMOS process and 3D IC technology. He has (co) authored over 150 articles in international refereed journals - Corresponding author: jlmeng@fudan.edu.cn; linchen@fudan.edu.cn
- Revised Date: 2024-05-10 Available Online: 2024-06-04
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Abstract
Photoelectric synaptic devices could emulate synaptic behaviors utilizing photoelectric effects and offer promising prospects with their high-speed operation and low crosstalk. In this study, we introduced a novel InGaZnO-based photoelectric memristor. Under both electrical and optical stimulation, the device successfully emulated synaptic characteristics including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation (LTP), and long-term depression (LTD). Furthermore, we demonstrated the practical application of our synaptic devices through the recognition of handwritten digits. The devices have successfully shown their ability to modulate synaptic weights effectively through light pulse stimulation, resulting in a recognition accuracy of up to 93.4%. The results illustrated the potential of IGZO-based memristors in neuromorphic computing, particularly their ability to simulate synaptic functionalities and contribute to image recognition tasks. -
References
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