Wang, Qinan, Zhao, Tianshi, Zhao, Chun, Liu, Wen, Yang, Li, Liu, Yina, Sheng, Dian, Xu, Rongxuan, Ge, Yutong, Tu, Xin 
ORCID: 0000-0002-6376-0897 et al (show 2 more authors)
(2022)
Solid‐State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing.
Advanced Electronic Materials, 8 (7).
p. 2101260.
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Advanced Electronic Materials 2101260.pdf - Author Accepted Manuscript Download (7MB) | Preview |
Abstract
<jats:title>Abstract</jats:title><jats:p>As the core component of an intelligent neuromorphic computer system, reliable synaptic devices process vast amounts of data with high computing speed and low energy consumption. In this work, the ion‐doped eco‐friendly solution‐processed indium oxide (InO<jats:sub>x</jats:sub>)/aluminum oxide (AlO<jats:sub>x</jats:sub>) electrolyte gate transistors (EGTs) with typical and reliable synaptic behavior are proposed. The lithium ions doped into the AlO<jats:sub>x</jats:sub> solid‐state layer to facilitate the generation of electrical double layers and doped into InO<jats:sub>x</jats:sub> to improve the stability of long‐term potentiation/depression cyclic update and enhance the synaptic plasticity. Finally, an artificial neural network simulator is well designed to electrocardiogram signal recognition based on the <jats:italic>G</jats:italic><jats:sub>max</jats:sub>/<jats:italic>G</jats:italic><jats:sub>min</jats:sub> ratio and nonlinearity of weight update curve. According to the results, the device possesses tremendous potential for biosignal prediction and neural intervention. Moreover, for the first time, the recognition accuracy of the abnormality of the cardiovascular can reach over 94.8% obtained from the confusion matrix. Consequently, this research article presents a stable and robust neuromorphic device for biosignal recognition based on solid‐state EGTs via the synaptic long‐term plasticity.</jats:p>
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | in-memory computing, neuromorphic computing, recognition of image and ECG, synaptic transistor |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science and Engineering > School of Physical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 19 Apr 2022 15:46 |
| Last Modified: | 04 Sep 2023 03:11 |
| DOI: | 10.1002/aelm.202101260 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3153226 |
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