Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

Yizhe Guo, Fan Wu, Guan-Hua Dun, Tianrui Cui, Yanming Liu, Xichao Tan, Yancong Qiao, Mario Lanza, He Tian, Yi Yang, Tian-Ling Ren

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)2208055
JournalAdvanced Functional Materials
DOIs
StatePublished - Dec 12 2022

ASJC Scopus subject areas

  • Biomaterials
  • Electrochemistry
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Fingerprint

Dive into the research topics of 'Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing'. Together they form a unique fingerprint.

Cite this