神经形态工程学
材料科学
光电子学
计算机科学
人工神经网络
晶体管
铟
人工智能
电气工程
电压
工程类
作者
Qinan Wang,Tianshi Zhao,Chun Zhao,Wen Liu,Li Yang,Yina Liu,Dian Sheng,Rongxuan Xu,Yutong Ge,Xin Tu,Hao Gao,Cezhou Zhao
标识
DOI:10.1002/aelm.202101260
摘要
Abstract 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 x )/aluminum oxide (AlO x ) electrolyte gate transistors (EGTs) with typical and reliable synaptic behavior are proposed. The lithium ions doped into the AlO x solid‐state layer to facilitate the generation of electrical double layers and doped into InO x 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 G max / G min 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.
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