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Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning cir...

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Autores principales: Ji, Xudong, Paulsen, Bryan D., Chik, Gary K. K., Wu, Ruiheng, Yin, Yuyang, Chan, Paddy K. L., Rivnay, Jonathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087835/
https://www.ncbi.nlm.nih.gov/pubmed/33931638
http://dx.doi.org/10.1038/s41467-021-22680-5
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author Ji, Xudong
Paulsen, Bryan D.
Chik, Gary K. K.
Wu, Ruiheng
Yin, Yuyang
Chan, Paddy K. L.
Rivnay, Jonathan
author_facet Ji, Xudong
Paulsen, Bryan D.
Chik, Gary K. K.
Wu, Ruiheng
Yin, Yuyang
Chan, Paddy K. L.
Rivnay, Jonathan
author_sort Ji, Xudong
collection PubMed
description Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.
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spelling pubmed-80878352021-05-11 Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor Ji, Xudong Paulsen, Bryan D. Chik, Gary K. K. Wu, Ruiheng Yin, Yuyang Chan, Paddy K. L. Rivnay, Jonathan Nat Commun Article Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications. Nature Publishing Group UK 2021-04-30 /pmc/articles/PMC8087835/ /pubmed/33931638 http://dx.doi.org/10.1038/s41467-021-22680-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ji, Xudong
Paulsen, Bryan D.
Chik, Gary K. K.
Wu, Ruiheng
Yin, Yuyang
Chan, Paddy K. L.
Rivnay, Jonathan
Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
title Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
title_full Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
title_fullStr Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
title_full_unstemmed Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
title_short Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
title_sort mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087835/
https://www.ncbi.nlm.nih.gov/pubmed/33931638
http://dx.doi.org/10.1038/s41467-021-22680-5
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