Electrospun Nanofiber‐Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing. (12th December 2022)

• Record Type: Journal Article
• Authors: Guo, Yizhe; Wu, Fan; Dun, Guan‐Hua; Cui, Tianrui; Liu, Yanming; Tan, Xichao; Qiao, Yancong; Lanza, Mario; Tian, He; Yang, Yi; Ren, Tian‐Ling
• Is Part Of: Advanced functional materials. Volume 33:Number 5(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-12
• Subjects: artificial synapses -- electrospun nanofibers -- field‐effect transistors -- neuromorphic computing -- short‐term plasticity; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202208055
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract: Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three‐terminal field‐effect transistors with gate‐controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber‐based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber‐based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short‐term plasticity behaviors including paired‐pulse facilitation, spike duration‐dependent plasticity, and dynamic filtering are tuned in this fiber‐based device. Furthermore, Perovskite‐doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next‐generation neuromorphic networks. Abstract : An electrospun ZnO nanofiber‐based transistor with tunable plasticity is fabricated by changing the technological parameters and the underlying mechanism of influence of carrier concentration and mobility on the device is revealed. Ultralow energy consumption and handwritten recognition application with high accuracy are realized in the perovskite‐doped devices to lay the foundation for next‐generation neuromorphic networks.