Bio-Inspired Photoelectric Artificial Synapse based on Two-Dimensional Ti3C2Tx MXenes Floating Gate

Tianshi Zhao, Chun Zhao*, Wangying Xu, Yina Liu, Hao Gao, Ivona Z. Mitrovic, Eng Gee Lim, Li Yang, Ce Zhou Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)

Abstract

The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.

Original languageEnglish
Article number2106000
JournalAdvanced Functional Materials
Volume31
Issue number45
DOIs
Publication statusPublished - 3 Nov 2021

Keywords

  • MXenes
  • image recognition
  • neuromorphic computing
  • photoelectric plasticity
  • synaptic transistors

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