Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783419274115678208 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6523178 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kimguhyun artificialneuralnetworkforresponseinferenceofanonvolatileresistanceswitcharray AT kornijcukvladimir artificialneuralnetworkforresponseinferenceofanonvolatileresistanceswitcharray AT kimdohun artificialneuralnetworkforresponseinferenceofanonvolatileresistanceswitcharray AT kiminho artificialneuralnetworkforresponseinferenceofanonvolatileresistanceswitcharray AT hwangcheolseong artificialneuralnetworkforresponseinferenceofanonvolatileresistanceswitcharray AT jeongdooseok artificialneuralnetworkforresponseinferenceofanonvolatileresistanceswitcharray |