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Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks

Yttrium oxide (Y(2)O(3)) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance...

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Autores principales: Lee, Taehun, Kim, Hae-In, Cho, Yoonjin, Lee, Sangwoo, Lee, Won-Yong, Bae, Jin-Hyuk, Kang, In-Man, Kim, Kwangeun, Lee, Sin-Hyung, Jang, Jaewon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490495/
https://www.ncbi.nlm.nih.gov/pubmed/37686940
http://dx.doi.org/10.3390/nano13172432
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author Lee, Taehun
Kim, Hae-In
Cho, Yoonjin
Lee, Sangwoo
Lee, Won-Yong
Bae, Jin-Hyuk
Kang, In-Man
Kim, Kwangeun
Lee, Sin-Hyung
Jang, Jaewon
author_facet Lee, Taehun
Kim, Hae-In
Cho, Yoonjin
Lee, Sangwoo
Lee, Won-Yong
Bae, Jin-Hyuk
Kang, In-Man
Kim, Kwangeun
Lee, Sin-Hyung
Jang, Jaewon
author_sort Lee, Taehun
collection PubMed
description Yttrium oxide (Y(2)O(3)) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the Y(2)O(3) RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within Y(2)O(3) leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated Y(2)O(3) RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the Y(2)O(3) RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y(2)O(3) RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems.
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spelling pubmed-104904952023-09-09 Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks Lee, Taehun Kim, Hae-In Cho, Yoonjin Lee, Sangwoo Lee, Won-Yong Bae, Jin-Hyuk Kang, In-Man Kim, Kwangeun Lee, Sin-Hyung Jang, Jaewon Nanomaterials (Basel) Article Yttrium oxide (Y(2)O(3)) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the Y(2)O(3) RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within Y(2)O(3) leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated Y(2)O(3) RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the Y(2)O(3) RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y(2)O(3) RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems. MDPI 2023-08-27 /pmc/articles/PMC10490495/ /pubmed/37686940 http://dx.doi.org/10.3390/nano13172432 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Taehun
Kim, Hae-In
Cho, Yoonjin
Lee, Sangwoo
Lee, Won-Yong
Bae, Jin-Hyuk
Kang, In-Man
Kim, Kwangeun
Lee, Sin-Hyung
Jang, Jaewon
Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks
title Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks
title_full Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks
title_fullStr Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks
title_full_unstemmed Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks
title_short Sol–Gel-Processed Y(2)O(3) Multilevel Resistive Random-Access Memory Cells for Neural Networks
title_sort sol–gel-processed y(2)o(3) multilevel resistive random-access memory cells for neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490495/
https://www.ncbi.nlm.nih.gov/pubmed/37686940
http://dx.doi.org/10.3390/nano13172432
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