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Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array

An artificial neural network was utilized in the behavior inference of a random crossbar array (10 × 9 or 28 × 27 in size) of nonvolatile binary resistance-switches (in a high resistance state (HRS) or low resistance state (LRS)) in response to a randomly applied voltage array. The employed artifici...

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Autores principales: Kim, Guhyun, Kornijcuk, Vladimir, Kim, Dohun, Kim, Inho, Hwang, Cheol Seong, Jeong, Doo Seok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523178/
https://www.ncbi.nlm.nih.gov/pubmed/30934793
http://dx.doi.org/10.3390/mi10040219
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author Kim, Guhyun
Kornijcuk, Vladimir
Kim, Dohun
Kim, Inho
Hwang, Cheol Seong
Jeong, Doo Seok
author_facet Kim, Guhyun
Kornijcuk, Vladimir
Kim, Dohun
Kim, Inho
Hwang, Cheol Seong
Jeong, Doo Seok
author_sort Kim, Guhyun
collection PubMed
description An artificial neural network was utilized in the behavior inference of a random crossbar array (10 × 9 or 28 × 27 in size) of nonvolatile binary resistance-switches (in a high resistance state (HRS) or low resistance state (LRS)) in response to a randomly applied voltage array. The employed artificial neural network was a multilayer perceptron (MLP) with leaky rectified linear units. This MLP was trained with 500,000 or 1,000,000 examples. For each example, an input vector consisted of the distribution of resistance states (HRS or LRS) over a crossbar array plus an applied voltage array. That is, for a M × N array where voltages are applied to its M rows, the input vector was M × (N + 1) long. The calculated (correct) current array for each random crossbar array was used as data labels for supervised learning. This attempt was successful such that the correlation coefficient between inferred and correct currents reached 0.9995 for the larger crossbar array. This result highlights MLP that leverages its versatility to capture the quantitative linkage between input and output across the highly nonlinear crossbar array.
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spelling pubmed-65231782019-06-03 Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array Kim, Guhyun Kornijcuk, Vladimir Kim, Dohun Kim, Inho Hwang, Cheol Seong Jeong, Doo Seok Micromachines (Basel) Article An artificial neural network was utilized in the behavior inference of a random crossbar array (10 × 9 or 28 × 27 in size) of nonvolatile binary resistance-switches (in a high resistance state (HRS) or low resistance state (LRS)) in response to a randomly applied voltage array. The employed artificial neural network was a multilayer perceptron (MLP) with leaky rectified linear units. This MLP was trained with 500,000 or 1,000,000 examples. For each example, an input vector consisted of the distribution of resistance states (HRS or LRS) over a crossbar array plus an applied voltage array. That is, for a M × N array where voltages are applied to its M rows, the input vector was M × (N + 1) long. The calculated (correct) current array for each random crossbar array was used as data labels for supervised learning. This attempt was successful such that the correlation coefficient between inferred and correct currents reached 0.9995 for the larger crossbar array. This result highlights MLP that leverages its versatility to capture the quantitative linkage between input and output across the highly nonlinear crossbar array. MDPI 2019-03-27 /pmc/articles/PMC6523178/ /pubmed/30934793 http://dx.doi.org/10.3390/mi10040219 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
Kim, Guhyun
Kornijcuk, Vladimir
Kim, Dohun
Kim, Inho
Hwang, Cheol Seong
Jeong, Doo Seok
Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array
title Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array
title_full Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array
title_fullStr Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array
title_full_unstemmed Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array
title_short Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array
title_sort artificial neural network for response inference of a nonvolatile resistance-switch array
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523178/
https://www.ncbi.nlm.nih.gov/pubmed/30934793
http://dx.doi.org/10.3390/mi10040219
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