Two-Dimensional Zeolitic Imidazolate Framework Based Optoelectronic Synaptic Transistor

作者：Ziqi Jia, Wen‐Min Zhong, Kui Zhou, Wei Xue, Yan Li, Zihao Feng, Haozhe Xue, Pengfei Zhao, Xue Chen, Hongxiang Wang, Xingke Cai, Shuangmei Xue, Yongbiao Zhai, Ziyu Lv, Yan Yan, Meng Zhang, Xueqing Yang, Guanglong Ding, Su‐Ting Han, Chi‐Ching Kuo, Ye Zhou · 发表于：The Journal of Physical Chemistry Letters · 年份：2025 · DOI：10.1021/acs.jpclett.5c00009 · 被引用次数：5 · 研究领域：Advanced Memory and Neural Computing、Quantum-Dot Cellular Automata、Photoreceptor and optogenetics research

Neuromorphic computing systems that integrate memory and computation offer a solution to the limitations of traditional von Neumann architectures. Optoelectronic synaptic transistors, responding to both optical and electrical signals, enable multifunctional operation with low power consumption. However, challenges such as short data retention and low processing efficiency remain. This study presents an optoelectronic synaptic transistor utilizing two-dimensional (2D) MoS 2, 2D zeolitic imidazolate framework (ZIF) Zn 2 (bim) 4, and gold (Au) nanoparticles (NPs) as semiconductor, tunneling layer, and floating gate materials, respectively. By adjusting the tunneling layer thickness, the charge-blocking capacity of Zn 2 (bim) 4 is modulated, improving long-term data retention. The optoelectronic properties of MoS 2 and the charge-trapping ability of Au NPs enable the transistor to mimic synaptic behaviors such as postsynaptic current (PSC), long-term potentiation (LTP), and transition from short-term to long-term memory (STM-LTM). This device can also be integrated into an artificial neural network (ANN) for smart healthcare applications, achieving 88.1% accuracy in electrocardiogram classification through optoelectronic dual-mode stimulation.