Dynamical characteristics of a nano-ionic solid electrolyte FET using an LSTM model

Gaurav, A., Song, X., Manhas, S.K. et al. (1 more author) (2025) Dynamical characteristics of a nano-ionic solid electrolyte FET using an LSTM model. In: 2024 IEEE Nanotechnology Materials and Devices Conference (NMDC). 19th IEEE Nanotechnology Materials and Devices Conference (IEEE NMDC 2024), 21-24 Oct 2024, Salt Lake City, Utah, USA. Institute of Electrical and Electronics Engineers (IEEE) , pp. 45-48. ISBN 979-8-3315-4144-6

Abstract

The complexity of multi-state devices (e.g., memristors, ferroelectric RAMs (FERAMs) hinder the creation of their unified physics-based model. Data-driven approaches, such as machine learning (ML), are increasingly favored to address this challenge. In this study, we demonstrate the dynamic modelling of a synaptic ZnO/Ta2O5 Solid Electrolyte-FET by transforming its characteristics into a multivariate time-series problem based on which a LongShort Term Memory model of the device is constructed. Our method can also be applied to other multi-state devices to accelerate the development time of Neuromorphic Computing Systems.

Metadata

Item Type:	Proceedings Paper
Authors/Creators:	Gaurav, A. Song, X. Manhas, S.K. De Souza, M.M. https://orcid.org/0000-0002-7804-7154
Copyright, Publisher and Additional Information:	© 2025 The Author(s). The Authors. Except as otherwise noted, this author-accepted version of a conference paper published in 2024 IEEE Nanotechnology Materials and Devices Conference (NMDC) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Keywords:	Solid modeling; Nonvolatile memory; Ferroelectric films; Reviews; Computational modeling; Time series analysis; Random access memory; Switches; Solids; Long short term memory
Dates:	Published: 25 February 2025 Published (online): 25 February 2025 Accepted: 2024
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering
Funding Information:	Funder Grant number UK RESEARCH AND INNOVATION EP/Y029763/1
Depositing User:	Symplectic Sheffield
Date Deposited:	15 Jan 2025 10:31
Last Modified:	03 Mar 2025 15:01
Status:	Published
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Refereed:	Yes
Identification Number:	10.1109/NMDC58214.2024.10894584
Related URLs:	Conference
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:221346

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Dynamical characteristics of a nano-ionic solid electrolyte FET using an LSTM model

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