Bio-Inspired Photoelectric Artificial Synapse based on Two-Dimensional Ti<sub>3</sub>C<sub>2</sub>T<i><sub>x</sub></i> MXenes Floating Gate



Zhao, Tianshi, Zhao, Chun, Xu, Wangying, Liu, Yina, Gao, Hao, Mitrovic, Ivona Z ORCID: 0000-0003-4816-8905, Lim, Eng Gee ORCID: 0000-0003-0199-7386, Yang, Li and Zhao, Ce Zhou
(2021) Bio-Inspired Photoelectric Artificial Synapse based on Two-Dimensional Ti<sub>3</sub>C<sub>2</sub>T<i><sub>x</sub></i> MXenes Floating Gate. ADVANCED FUNCTIONAL MATERIALS, 31 (45). p. 2106000.

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Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

Item Type: Article
Uncontrolled Keywords: image recognition, MXenes, neuromorphic computing, photoelectric plasticity, synaptic transistors
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 28 Jan 2022 11:01
Last Modified: 17 Oct 2023 21:29
DOI: 10.1002/adfm.202106000
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3147763