期刊
NPJ COMPUTATIONAL MATERIALS
卷 6, 期 1, 页码 -出版社
NATURE RESEARCH
DOI: 10.1038/s41524-020-00455-8
关键词
-
资金
- Faculty Science and Technology Acquisition and Retention (STARs) Program in the University of Texas System
- University of Texas at Arlington
- Center for Nanophase Materials Sciences, DOE Office of Science User Facility
- Army Research Office [W911NF-17-1-0462]
- Computational Materials Sciences Program - US Department of Energy, Office of Science, Basic Energy Sciences [DE-SC0020145]
- Donald W. Hamer Foundation through the Hamer Professorship at Penn State
- National Natural Science Foundation of China [51802280]
- U.S. Department of Energy [DE-AC05-00OR22725]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据