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Novel synaptic memory device for neuromorphic computing

This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO(2) material. The mechanism behind re...

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Autores principales: Mandal, Saptarshi, El-Amin, Ammaarah, Alexander, Kaitlyn, Rajendran, Bipin, Jha, Rashmi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061545/
https://www.ncbi.nlm.nih.gov/pubmed/24939247
http://dx.doi.org/10.1038/srep05333
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author Mandal, Saptarshi
El-Amin, Ammaarah
Alexander, Kaitlyn
Rajendran, Bipin
Jha, Rashmi
author_facet Mandal, Saptarshi
El-Amin, Ammaarah
Alexander, Kaitlyn
Rajendran, Bipin
Jha, Rashmi
author_sort Mandal, Saptarshi
collection PubMed
description This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO(2) material. The mechanism behind reconfiguration was studied and a unified model is presented to explain the underlying device physics. The model was then utilized to show the application of these devices in speech recognition. A comparison between a 20 nm × 20 nm sized synaptic memory device with that of a state-of-the-art VLSI SRAM synapse showed ~10× reduction in area and >10(6) times reduction in the power consumption per learning cycle.
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spelling pubmed-40615452014-06-18 Novel synaptic memory device for neuromorphic computing Mandal, Saptarshi El-Amin, Ammaarah Alexander, Kaitlyn Rajendran, Bipin Jha, Rashmi Sci Rep Article This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO(2) material. The mechanism behind reconfiguration was studied and a unified model is presented to explain the underlying device physics. The model was then utilized to show the application of these devices in speech recognition. A comparison between a 20 nm × 20 nm sized synaptic memory device with that of a state-of-the-art VLSI SRAM synapse showed ~10× reduction in area and >10(6) times reduction in the power consumption per learning cycle. Nature Publishing Group 2014-06-18 /pmc/articles/PMC4061545/ /pubmed/24939247 http://dx.doi.org/10.1038/srep05333 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
spellingShingle Article
Mandal, Saptarshi
El-Amin, Ammaarah
Alexander, Kaitlyn
Rajendran, Bipin
Jha, Rashmi
Novel synaptic memory device for neuromorphic computing
title Novel synaptic memory device for neuromorphic computing
title_full Novel synaptic memory device for neuromorphic computing
title_fullStr Novel synaptic memory device for neuromorphic computing
title_full_unstemmed Novel synaptic memory device for neuromorphic computing
title_short Novel synaptic memory device for neuromorphic computing
title_sort novel synaptic memory device for neuromorphic computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061545/
https://www.ncbi.nlm.nih.gov/pubmed/24939247
http://dx.doi.org/10.1038/srep05333
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