High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory

Abstract Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a ph...

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Autores principales:	Kena Zhang, Jianjun Wang, Yuhui Huang, Long-Qing Chen, P. Ganesh, Ye Cao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/a30904cc19e94eaf90e48eac97ae9b94
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spelling	oai:doaj.org-article:a30904cc19e94eaf90e48eac97ae9b942021-12-02T12:40:37ZHigh-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory10.1038/s41524-020-00455-82057-3960https://doaj.org/article/a30904cc19e94eaf90e48eac97ae9b942020-12-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00455-8https://doaj.org/toc/2057-3960Abstract Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO2−x systems such as HfO2−x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.Kena ZhangJianjun WangYuhui HuangLong-Qing ChenP. GaneshYe CaoNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 6, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Kena Zhang Jianjun Wang Yuhui Huang Long-Qing Chen P. Ganesh Ye Cao High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
description	Abstract Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO2−x systems such as HfO2−x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.
format	article
author	Kena Zhang Jianjun Wang Yuhui Huang Long-Qing Chen P. Ganesh Ye Cao
author_facet	Kena Zhang Jianjun Wang Yuhui Huang Long-Qing Chen P. Ganesh Ye Cao
author_sort	Kena Zhang
title	High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
title_short	High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
title_full	High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
title_fullStr	High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
title_full_unstemmed	High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
title_sort	high-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a30904cc19e94eaf90e48eac97ae9b94
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High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory

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