期刊
JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY
卷 119, 期 -, 页码 150-155出版社
JOURNAL MATER SCI TECHNOL
DOI: 10.1016/j.jmst.2021.12.016
关键词
Artificial synapse; Neuromorphic computing; Oxide semiconductor; Proton conductor; Artificial neural network
资金
- National Research Foundation of Korea (NRF) - Korean government (MSIT) [2020R1A2C2004864]
- National Research Foundation of Korea [2020R1A2C2004864] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This study presents ZnO-based artificial synapses with peptide insulators for emulating biological synapses. The device demonstrates time-dependent responses and memory transition via proton doping. Furthermore, it shows potential for image classification.
Neuromorphic devices inspired by the human brain have attracted significant attention because of their excellent ability for cognitive and parallel computing. This study presents ZnO-based artificial synapses with peptide insulators for the electrical emulation of biological synapses. We demonstrated the dynamic responses of the device under various environmental conditions. The proton-conducting property of the tyrosine-rich peptide enables time-dependent responses under ambient conditions such that various aspects of synaptic behaviors are emulated by the devices. The transition from short-term memory to longterm memory is achieved via electrochemical doping of ZnO by protons. Furthermore, we demonstrate an image classification simulation using a multi-layer perceptron model to evaluate the potential of the device for use in neuromorphic computing. The neural network based on our device achieved a recognition accuracy of 87.47% for the MNIST handwritten digit images. This work proposes a novel device platform inspired by biosystems for brain-mimetic hardware systems. (c) 2022 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
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