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.
| Original language | English |
|---|---|
| Article number | 198 |
| Journal | npj Computational Materials |
| Volume | 6 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2020 |
Funding
Y.C. acknowledges supports from the Faculty Science and Technology Acquisition and Retention (STARs) Program in the University of Texas System, and the startup funding from the University of Texas at Arlington. K.Z. and Y.C. acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper (http://www.tacc.utexas.edu). P.G. was supported by the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. J.-J.W. acknowledges the partial support from the Army Research Office under grant number W911NF-17-1-0462. L.Q. Chen acknowledges partial support from the Computational Materials Sciences Program funded by the US Department of Energy, Office of Science, Basic Energy Sciences, under Award Number DE-SC0020145. J.-J.W. and L.-Q.C. also acknowledges the partial support from the Donald W. Hamer Foundation through the Hamer Professorship at Penn State. Y.H.H. acknowledges support from National Natural Science Foundation of China under grant number 51802280. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy. gov/downloads/doe-public-access-plan).
| Funders | Funder number |
|---|---|
| Center for Nanophase Materials Sciences | |
| Donald W. Hamer Foundation | |
| Faculty Science and Technology Acquisition and Retention | |
| U.S. Department of Energy | |
| Army Research Office | W911NF-17-1-0462 |
| Office of Science | |
| Basic Energy Sciences | DE-SC0020145 |
| University of Texas System | |
| Pennsylvania State University | |
| University of Texas at Arlington | |
| National Natural Science Foundation of China | DE-AC05-00OR22725, 51802280 |