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Robust Memristor Networks for Neuromorphic Computation Applications

One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadv...

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Detalles Bibliográficos
Autores principales: Hajtó, Dániel, Rák, Ádám, Cserey, György
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862673/
https://www.ncbi.nlm.nih.gov/pubmed/31683537
http://dx.doi.org/10.3390/ma12213573
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author Hajtó, Dániel
Rák, Ádám
Cserey, György
author_facet Hajtó, Dániel
Rák, Ádám
Cserey, György
author_sort Hajtó, Dániel
collection PubMed
description One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.
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spelling pubmed-68626732019-12-05 Robust Memristor Networks for Neuromorphic Computation Applications Hajtó, Dániel Rák, Ádám Cserey, György Materials (Basel) Article One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications. MDPI 2019-10-31 /pmc/articles/PMC6862673/ /pubmed/31683537 http://dx.doi.org/10.3390/ma12213573 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hajtó, Dániel
Rák, Ádám
Cserey, György
Robust Memristor Networks for Neuromorphic Computation Applications
title Robust Memristor Networks for Neuromorphic Computation Applications
title_full Robust Memristor Networks for Neuromorphic Computation Applications
title_fullStr Robust Memristor Networks for Neuromorphic Computation Applications
title_full_unstemmed Robust Memristor Networks for Neuromorphic Computation Applications
title_short Robust Memristor Networks for Neuromorphic Computation Applications
title_sort robust memristor networks for neuromorphic computation applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862673/
https://www.ncbi.nlm.nih.gov/pubmed/31683537
http://dx.doi.org/10.3390/ma12213573
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