AlGaN/GaN MOS-HEMT enabled optoelectronic artificial synaptic devices for neuromorphic computing

• Published in: APL machine learning 2024-06, Vol.2 (2), p.026113-026113-9
• Main Authors: Chen, Jiaxiang, Du, Haitao, Qu, Haolan, Gao, Han, Gu, Yitian, Zhu, Yitai, Ye, Wenbo, Zou, Jun, Wang, Hongzhi, Zou, Xinbo
• Format: Article
• Language: English
• ISSN: 2770-9019; 2770-9019
• DOI: 10.1063/5.0194083

Artificial optoelectronic synaptic transistors have attracted extensive research interest as an essential component for neuromorphic computing systems and brain emulation applications. However, performance challenges still remain for synaptic devices, including low energy consumption, high integration density, and flexible modulation. Employing trapping and detrapping relaxation, a novel optically stimulated synaptic transistor enabled by the AlGaN/GaN hetero-structure metal-oxide semiconductor high-electron-mobility transistor has been successfully demonstrated in this study. Synaptic functions, including excitatory postsynaptic current (EPSC), paired-pulse facilitation index, and transition from short-term memory to long-term memory, are well mimicked and explicitly investigated. In a single EPSC event, the AlGaN/GaN synaptic transistor shows the characteristics of low energy consumption and a high signal-to-noise ratio. The EPSC of the synaptic transistor can be synergistically modulated by both optical stimulation and gate/drain bias. Moreover, utilizing a convolution neural network, hand-written digit images were used to verify the data preprocessing capability for neuromorphic computing applications.