Bi-mode electrolyte-gated synaptic transistor via additional ion doping and its application to artificial nociceptors. Issue 10 (26th August 2021)

• Record Type: Journal Article
• Authors: Yu, Rengjian; Yan, Yujie; Li, Enlong; Wu, Xiaomin; Zhang, Xianghong; Chen, Jinwei; Hu, Yuanyuan; Chen, Huipeng; Guo, Tailiang
• Is Part Of: Materials horizons. Volume 8:Issue 10(2021)
• Page Start: 2797
• Page End: 2807
• Publication Date: 2021-08-26
• Subjects: Materials -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d1mh01061a
• Languages: English
• ISSNs: 2051-6347

Abstract

Abstract : This work proposed a bi-mode electrolyte-gated synaptic transistor. Benefiting from two controllable dynamic processes, this device could achieve multilevel modulation of sensitivity, which can serve as a stable artificial nociceptor. Abstract : Multiple types of synaptic transistors that are capable of processing electrical signals similar to the biological neural system hold enormous potential for application in parallel computing, logic circuits and peripheral detection. However, most of these presented synaptic transistors are confined to a single mode of synaptic plasticity under an electrical stimulus, which tremendously limits efficient memory formation and the multifunctional integration of synaptic transistors. Here, we proposed a bi-mode electrolyte-gated synaptic transistor (BEST) with two dynamic processes, the formation of an electrical double layer (EDL) and electrochemical doping (ECD) by tuning the applied voltages, thereby allowing volatile and non-volatile behavior, which is associated with additional ion doping and nanoscale ionic transport. Benefiting from two controllable dynamic processes, we surprisingly found a third state in the transfer curves besides the "off" and "on" states. Moreover, utilizing this unique property, an artificial nociceptor with multilevel modulation of sensitivity was realized based on our bi-mode device. Finally, a haptic sensory system was constructed to exhibit robotic motion that revealed a unique threshold switching behavior, indicating the applicability to peripheral sensing circuits. Hence, the presented bi-mode synaptic transistor provides promising prospects in achieving multiple-mode integrated devices and simplifying neural circuits, which shows great potential in the development of artificial intelligence.