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Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing

Here, various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors (memTs) with an unconventional redox-gating mechanism are demonstrated. Our synaptic memT device using conjugated polymer thin-film and redox-active solid e...

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Autores principales: Sagar, Srikrishna, Udaya Mohanan, Kannan, Cho, Seongjae, Majewski, Leszek A., Das, Bikas C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907356/
https://www.ncbi.nlm.nih.gov/pubmed/35264605
http://dx.doi.org/10.1038/s41598-022-07505-9
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author Sagar, Srikrishna
Udaya Mohanan, Kannan
Cho, Seongjae
Majewski, Leszek A.
Das, Bikas C.
author_facet Sagar, Srikrishna
Udaya Mohanan, Kannan
Cho, Seongjae
Majewski, Leszek A.
Das, Bikas C.
author_sort Sagar, Srikrishna
collection PubMed
description Here, various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors (memTs) with an unconventional redox-gating mechanism are demonstrated. Our synaptic memT device using conjugated polymer thin-film and redox-active solid electrolyte as the gate dielectric can be routinely operated at gate voltages (V(GS)) below − 1.5 V, subthreshold-swings (S) smaller than 120 mV/dec, and ON/OFF current ratio larger than 10(8). Large hysteresis in transfer curves depicts the signature of non-volatile resistive switching (RS) property with ON/OFF ratio as high as 10(5). In addition, our memT device also shows many synaptic functions, including the availability of many conducting-states (> 500) that are used for efficient pattern recognition using the simplest neural network simulation model with training and test accuracy higher than 90%. Overall, the presented approach opens a new and promising way to fabricate high-performance artificial synapses and their arrays for the implementation of hardware-oriented neural network.
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spelling pubmed-89073562022-03-11 Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing Sagar, Srikrishna Udaya Mohanan, Kannan Cho, Seongjae Majewski, Leszek A. Das, Bikas C. Sci Rep Article Here, various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors (memTs) with an unconventional redox-gating mechanism are demonstrated. Our synaptic memT device using conjugated polymer thin-film and redox-active solid electrolyte as the gate dielectric can be routinely operated at gate voltages (V(GS)) below − 1.5 V, subthreshold-swings (S) smaller than 120 mV/dec, and ON/OFF current ratio larger than 10(8). Large hysteresis in transfer curves depicts the signature of non-volatile resistive switching (RS) property with ON/OFF ratio as high as 10(5). In addition, our memT device also shows many synaptic functions, including the availability of many conducting-states (> 500) that are used for efficient pattern recognition using the simplest neural network simulation model with training and test accuracy higher than 90%. Overall, the presented approach opens a new and promising way to fabricate high-performance artificial synapses and their arrays for the implementation of hardware-oriented neural network. Nature Publishing Group UK 2022-03-09 /pmc/articles/PMC8907356/ /pubmed/35264605 http://dx.doi.org/10.1038/s41598-022-07505-9 Text en © The Author(s) 2022 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sagar, Srikrishna
Udaya Mohanan, Kannan
Cho, Seongjae
Majewski, Leszek A.
Das, Bikas C.
Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
title Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
title_full Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
title_fullStr Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
title_full_unstemmed Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
title_short Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
title_sort emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907356/
https://www.ncbi.nlm.nih.gov/pubmed/35264605
http://dx.doi.org/10.1038/s41598-022-07505-9
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