Reservoir computing for temporal data classification using a dynamic solid electrolyte ZnO thin film transistor

Gaurav, A., Song, X., Manhas, S. et al. (4 more authors) (2022) Reservoir computing for temporal data classification using a dynamic solid electrolyte ZnO thin film transistor. Frontiers in Electronics, 3. 869013.

Abstract

Metadata

Authors/Creators:
  • Gaurav, A.
  • Song, X.
  • Manhas, S.
  • Gilra, A.
  • Vasilaki, E.
  • Roy, P.
  • De Souza, M.M.
Copyright, Publisher and Additional Information: © 2022 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: reservoir computing; solid electrolyte devices; temporal data; short-term memory; neural networks; thin-film transistor
Dates:
  • Accepted: 18 March 2022
  • Published (online): 11 April 2022
  • Published: 11 April 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 05 May 2022 08:56
Last Modified: 05 May 2022 08:56
Status: Published
Publisher: Frontiers Media SA
Refereed: Yes
Identification Number: https://doi.org/10.3389/felec.2022.869013

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