沸石咪唑盐骨架
材料科学
晶体管
光电子学
咪唑酯
计算机科学
电气工程
化学
工程类
无机化学
金属有机骨架
电压
吸附
有机化学
作者
Ziqi Jia,Wen‐Min Zhong,Kui Zhou,Wei Zeng,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
标识
DOI:10.1021/acs.jpclett.5c00009
摘要
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) MoS2, 2D zeolitic imidazolate framework (ZIF) Zn2(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 Zn2(bim)4 is modulated, improving long-term data retention. The optoelectronic properties of MoS2 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.
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