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

Kena Zhang, Jianjun Wang, Yuhui Huang, Long Qing Chen, P. Ganesh, Ye Cao

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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 languageEnglish (US)
Article number198
Journalnpj Computational Materials
Issue number1
StatePublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • General Materials Science
  • Mechanics of Materials
  • Computer Science Applications


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