Two-Dimensional Zeolitic Imidazolate Framework Based Optoelectronic Synaptic Transistor

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<sub>2</sub>, 2D zeolitic imidazolate framework (ZIF) Zn<sub>2</sub>(bim)<sub>4</sub>, 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<sub>2</sub>(bim)<sub>4</sub> is modulated, improving long-term data retention. The optoelectronic properties of MoS<sub>2</sub> 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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Publication Details

Journal
The Journal of Physical Chemistry Letters
Published
2025-03-17
DOI
https://doi.org/10.1021/acs.jpclett.5c00009
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19

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article

Two-Dimensional Zeolitic Imidazolate Framework Based Optoelectronic Synaptic Transistor

Shuangmei Xue, Su‐Ting Han, Wen‐Min Zhong, Kui Zhou et al.
3 citations
The Journal of Physical Chemistry Letters
Advanced Memory and Neural Computing
2.19
article

Two-Dimensional Zeolitic Imidazolate Framework Based Optoelectronic Synaptic Transistor

Shuangmei Xue, Su‐Ting Han, Wen‐Min Zhong, Kui Zhou, Yongbiao Zhai, Chi‐Ching Kuo, Pengfei Zhao, Zihao Feng, Xingke Cai, Ziqi Jia, Guanglong Ding, Ziyu Lv, Xueqing Yang, Yan Yan, Xue Chen, Wei Zeng, Meng Zhang, Hongxiang Wang, Ye Bo Zhou, Haozhe Xue, Yan Li
article en
3 citations

Abstract

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<sub>2</sub>, 2D zeolitic imidazolate framework (ZIF) Zn<sub>2</sub>(bim)<sub>4</sub>, 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<sub>2</sub>(bim)<sub>4</sub> is modulated, improving long-term data retention. The optoelectronic properties of MoS<sub>2</sub> 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.

The Journal of Physical Chemistry LettersVol. 16(12)
National Taipei University of Technology (TW), Hong Kong Polytechnic University (HK), National Taiwan University (TW), Shenzhen University (CN), Changchun Normal University (CN), Soochow University (CN), Guangdong Province Special Equipment Testing and Research Institute Zhuhai Testing Institute (CN)
Royal Society of Chemistry, National Natural Science Foundation of China, National Taipei University of Technology, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 13%
Advanced Memory and Neural Computing
2.19
DOISourceOpenAlex
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