Solid‐State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing. (9th April 2022)

• Record Type: Journal Article
• Authors: Wang, Qinan; Zhao, Tianshi; Zhao, Chun; Liu, Wen; Yang, Li; Liu, Yina; Sheng, Dian; Xu, Rongxuan; Ge, Yutong; Tu, Xin; Gao, Hao; Zhao, Cezhou
• Is Part Of: Advanced Electronic Materials. Volume 8:Number 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-09
• Subjects: in‐memory computing -- neuromorphic computing -- recognition of image and ECG -- synaptic transistor; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.202101260
• Languages: English
• ISSNs: 2199-160X

Abstract

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 (InOx )/aluminum oxide (AlOx ) electrolyte gate transistors (EGTs) with typical and reliable synaptic behavior are proposed. The lithium ions doped into the AlOx solid‐state layer to facilitate the generation of electrical double layers and doped into InOx 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. Abstract : The neuromorphic computing for the prediction of cardiovascular abnormalities and recognition of matrix data based on eco‐friendly synaptic electrolyte gate transistor is proposed to realize the integrated storage and computing function. An artificial neural network simulator is well designed to realize digital image and electrocardiogram signal recognition based on the G max / G min and nonlinearity of the electrolyte gate transistors.