InGaZnO-based photoelectric synaptic devices for neuromorphic computing

作     者：Jieru Song Jialin Meng Tianyu Wang Changjin Wan Hao Zhu Qingqing Sun David Wei Zhang Lin Chen Jieru Song;Jialin Meng;Tianyu Wang;Changjin Wan;Hao Zhu;Qingqing Sun;David Wei Zhang;Lin Chen

作者机构：School of MicroelectronicsState Key Laboratory of Integrated Chips and SystemsFudan UniversityShanghai 200433China School of Integrated CircuitsShandong UniversityJinan 250100China National Integrated Circuit Innovation CenterShanghai 201203China School of Electronic Science and EngineeringNanjing UniversityNanjing 210023China Jiashan Fudan InstituteJiashan 314102China 

出 版 物：《Journal of Semiconductors》 (半导体学报（英文版）)

年 卷 期：2024年第45卷第9期

页      面：42-47页

核心收录：

学科分类：080903[工学-微电子学与固体电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the National Key Research and Development Program of China (2021YFA1202600) the NSFC (92064009, 22175042) the Science and Technology Commission of Shanghai Municipality (22501100900) the China Postdoctoral Science Foundation (2022TQ0068, 2023M740644) the Shanghai Sailing Program (23YF1402200, 23YF1402400) the Qilu Young Scholar Program of Shandong University 

主　　题：InGaZnO artificial synapse neuromorphic computing photoelectric memristor 

摘      要：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.