Journal of Inorganic Materials ›› 2023, Vol. 38 ›› Issue (4): 406-412.DOI: 10.15541/jim20220675

• Topical Section on Neuromorphic Materials and Devices (Contributing Editor: WAN Qing) • Previous Articles     Next Articles

Effect of Plasma Treatment on the Long-term Plasticity of Synaptic Transistor

QIU Haiyang(), MIAO Guangtan, LI Hui, LUAN Qi, LIU Guoxia, SHAN Fukai()   

  1. College of Microtechnology & Nanotechnology, Qingdao University, Qingdao 266071, China
  • Received:2022-11-14 Revised:2022-12-19 Published:2023-04-20 Online:2022-12-28
  • Contact: SHAN Fukai, professor. E-mail:
  • About author:QIU Haiyang (1997-), male, Master candidate. E-mail:
  • Supported by:
    National Key Research and Development Program(2019YE0121800);National Natural Science Foundation of China(51872149);Natural Science Foundation of Shandong Province(ZR2022MF246)


As the basic and essential unit of neuromorphic computing system, artificial synaptic devices exhibit great potential in accelerating the high-performance parallel computation, artificial intelligence, and adaptive learning. Among them, electrolyte-gated synaptic transistors (EGSTs) have received increasing attention as the next generation neuromorphic devices owing to its controllable channel conductance. The devices exhibit the abilities of simulating the short-term plasticity (STP) and long-term plasticity (LTP) of the neural synapses. However, most of EGSTs exhibit short persistence for LTP and their channel conductance is difficult to be adjusted due to the rapid self-discharge of the electric double layer. In this work, the EGSTs based on water-induced In2O3 as the channel and chitosan as gate electrolyte were constructed and the O2 plasma treatments were performed. The formation of traps on the channel surface is caused by the O2 plasma treatments, which leads to capturing hydrogen ions at interface of the electrolyte/channel layer, and the device performance exhibits an enlarged hysteresis window, so as to regulate LTP of EGSTs. Biological synaptic functions, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), STP, and LTP, were mimicked by electrochemical doping and electrostatic coupling effects. Meanwhile, based on the experimentally verified potentiation/depression characteristics of the EGSTs, a three-layer artificial neural network is applied for handwritten digit recognition, and simulation tests can obtain high recognition accuracy of 94.7%. These results reveal that surface plasma treatment is one of the key technologies to affect the device performance, which has great potential in regulating synaptic function of EGSTs.

Key words: electrolyte-gated synaptic transistor, synaptic plasticity, plasma treatment, pattern recognition

CLC Number: