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Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory
In this study, we constructed a voltage–time transformation model (V–t Model) to predict and simulate the spiking behavior of threshold-switching selector-based neurons (TS neurons). The V–t Model combines the physical nucleation theory and the resistor–capacitor (RC) equivalent circuit and successf...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043246/ https://www.ncbi.nlm.nih.gov/pubmed/35495030 http://dx.doi.org/10.3389/fnins.2022.868671 |
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author | Yap, Suk-Min Wang, I-Ting Wu, Ming-Hung Hou, Tuo-Hung |
author_facet | Yap, Suk-Min Wang, I-Ting Wu, Ming-Hung Hou, Tuo-Hung |
author_sort | Yap, Suk-Min |
collection | PubMed |
description | In this study, we constructed a voltage–time transformation model (V–t Model) to predict and simulate the spiking behavior of threshold-switching selector-based neurons (TS neurons). The V–t Model combines the physical nucleation theory and the resistor–capacitor (RC) equivalent circuit and successfully depicts the history-dependent threshold voltage of TS selectors, which has not yet been modeled in TS neurons. Moreover, based on our model, we analyzed the currently reported TS devices, including ovonic threshold switching (OTS), insulator-metal transition, and silver- (Ag-) based selectors, and compared the behaviors of the predicted neurons. The results suggest that the OTS neuron is the most promising and potentially achieves the highest spike frequency of GHz and the lowest operating voltage and area overhead. The proposed V–t Model provides an engineering pathway toward the future development of TS neurons for neuromorphic computing applications. |
format | Online Article Text |
id | pubmed-9043246 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90432462022-04-28 Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory Yap, Suk-Min Wang, I-Ting Wu, Ming-Hung Hou, Tuo-Hung Front Neurosci Neuroscience In this study, we constructed a voltage–time transformation model (V–t Model) to predict and simulate the spiking behavior of threshold-switching selector-based neurons (TS neurons). The V–t Model combines the physical nucleation theory and the resistor–capacitor (RC) equivalent circuit and successfully depicts the history-dependent threshold voltage of TS selectors, which has not yet been modeled in TS neurons. Moreover, based on our model, we analyzed the currently reported TS devices, including ovonic threshold switching (OTS), insulator-metal transition, and silver- (Ag-) based selectors, and compared the behaviors of the predicted neurons. The results suggest that the OTS neuron is the most promising and potentially achieves the highest spike frequency of GHz and the lowest operating voltage and area overhead. The proposed V–t Model provides an engineering pathway toward the future development of TS neurons for neuromorphic computing applications. Frontiers Media S.A. 2022-04-13 /pmc/articles/PMC9043246/ /pubmed/35495030 http://dx.doi.org/10.3389/fnins.2022.868671 Text en Copyright © 2022 Yap, Wang, Wu and Hou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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. |
spellingShingle | Neuroscience Yap, Suk-Min Wang, I-Ting Wu, Ming-Hung Hou, Tuo-Hung Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory |
title | Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory |
title_full | Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory |
title_fullStr | Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory |
title_full_unstemmed | Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory |
title_short | Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory |
title_sort | voltage–time transformation model for threshold switching spiking neuron based on nucleation theory |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043246/ https://www.ncbi.nlm.nih.gov/pubmed/35495030 http://dx.doi.org/10.3389/fnins.2022.868671 |
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