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Artificial synaptic characteristics of PVA:ZnO nanocomposite memristive devices

Kumari, Renu ; Gellanki, Jnaneswari ; Kundale, Somnath S. ; Ustad, Ruhan E. ; Dongale, Tukaram D. ; Fu, Ying ; Pettersson, Håkan LU and Kumar, Sandeep LU (2023) In APL Materials 11(10).
Abstract

Computational efficiency is significantly enhanced using artificial neural network-based computing. A two-terminal memristive device is a powerful electronic device that can mimic the behavior of a biological synapse in addition to storing information and performing logic operations. This work focuses on the fabrication of a memristive device that utilizes a resistive switching layer composed of polyvinyl alcohol infused with ZnO nanoparticles. By incorporating ZnO nanoparticles into the polymer film, the fabricated memristive devices exhibit functionalities that closely resemble those of biological synapses, including short-term and long-term plasticity, paired-pulse facilitation, and spike time-dependent plasticity. These findings... (More)

Computational efficiency is significantly enhanced using artificial neural network-based computing. A two-terminal memristive device is a powerful electronic device that can mimic the behavior of a biological synapse in addition to storing information and performing logic operations. This work focuses on the fabrication of a memristive device that utilizes a resistive switching layer composed of polyvinyl alcohol infused with ZnO nanoparticles. By incorporating ZnO nanoparticles into the polymer film, the fabricated memristive devices exhibit functionalities that closely resemble those of biological synapses, including short-term and long-term plasticity, paired-pulse facilitation, and spike time-dependent plasticity. These findings establish the ZnO nanoparticle-polymer nanocomposite as a highly promising material for future neuromorphic systems.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
APL Materials
volume
11
issue
10
article number
101124
publisher
American Institute of Physics (AIP)
external identifiers
  • scopus:85175313733
ISSN
2166-532X
DOI
10.1063/5.0165205
language
English
LU publication?
yes
id
7d1e8dde-417c-4836-8756-b36f48251d7b
date added to LUP
2023-12-12 13:58:54
date last changed
2024-02-09 11:01:27
@article{7d1e8dde-417c-4836-8756-b36f48251d7b,
  abstract     = {{<p>Computational efficiency is significantly enhanced using artificial neural network-based computing. A two-terminal memristive device is a powerful electronic device that can mimic the behavior of a biological synapse in addition to storing information and performing logic operations. This work focuses on the fabrication of a memristive device that utilizes a resistive switching layer composed of polyvinyl alcohol infused with ZnO nanoparticles. By incorporating ZnO nanoparticles into the polymer film, the fabricated memristive devices exhibit functionalities that closely resemble those of biological synapses, including short-term and long-term plasticity, paired-pulse facilitation, and spike time-dependent plasticity. These findings establish the ZnO nanoparticle-polymer nanocomposite as a highly promising material for future neuromorphic systems.</p>}},
  author       = {{Kumari, Renu and Gellanki, Jnaneswari and Kundale, Somnath S. and Ustad, Ruhan E. and Dongale, Tukaram D. and Fu, Ying and Pettersson, Håkan and Kumar, Sandeep}},
  issn         = {{2166-532X}},
  language     = {{eng}},
  number       = {{10}},
  publisher    = {{American Institute of Physics (AIP)}},
  series       = {{APL Materials}},
  title        = {{Artificial synaptic characteristics of PVA:ZnO nanocomposite memristive devices}},
  url          = {{http://dx.doi.org/10.1063/5.0165205}},
  doi          = {{10.1063/5.0165205}},
  volume       = {{11}},
  year         = {{2023}},
}