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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6862673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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