Affiliations 

  • 1 School of Computing and Information Sciences, Fuzhou Institute of Technology, Fuzhou, 350506, People's Republic of China
  • 2 Institute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
  • 3 School of Physics and Information Engineering, Fuzhou University, Fuzhou, 350002, People's Republic of China. chaoxing_wu@fzu.edu.cn
  • 4 Institute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia. pcooi@gmx.com
  • 5 School of Physics and Information Engineering, Fuzhou University, Fuzhou, 350002, People's Republic of China. fushanli@hotmail.com
Sci Rep, 2023 May 20;13(1):8194.
PMID: 37210533 DOI: 10.1038/s41598-023-35183-8

Abstract

Artificial electronic synapses are commonly used to simulate biological synapses to realize various learning functions, regarded as one of the key technologies in the next generation of neurological computation. This work used a simple spin coating technique to fabricate polyimide (PI):graphene quantum dots(GQDs) memristor structure. As a result, the devices exhibit remarkably stable exponentially decaying postsynaptic suppression current over time, as interpreted in the spike-timing-dependent plasticity phenomenon. Furthermore, with the increase of the applied electrical signal over time, the conductance of the electrical synapse gradually changes, and the electronic synapse also shows plasticity dependence on the amplitude and frequency of the pulse applied. In particular, the devices with the structure of Ag/PI:GQDs/ITO prepared in this study can produce a stable response to the stimulation of electrical signals between millivolt to volt, showing not only high sensitivity but also a wide range of "feelings", which makes the electronic synapses take a step forwards to emulate biological synapses. Meanwhile, the electronic conduction mechanisms of the device are also studied and expounded in detail. The findings in this work lay a foundation for developing brain-like neuromorphic modeling in artificial intelligence.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.