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

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

doi:10.1038/s41524-020-00455-8

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

Journal Article · Fri Dec 18 00:00:00 EST 2020 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-020-00455-8· OSTI ID:1787247

Zhang, Kena ^[1];

^[2]; Huang, Yuhui ^[3]; Chen, Long-Qing ^[2];

^[4];

^[1]

Univ. of Texas, Arlington, TX (United States). Dept. of Materials Science and Engineering
Pennsylvania State Univ., University Park, PA (United States). Dept. of Materials Science and Engineering
Zhejiang Univ., Hangzhou (China). School of Materials Science and Engineering. Cyrus Tang Center for Sensor Materials and Applications. State Key Lab. of Silicon Materials. Lab. of Dielectric Materials
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)

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 MO_2-x systems such as HfO_2-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.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); US Army Research Office (ARO); National Natural Science Foundation of China (NSFC)

Grant/Contract Number:: SC0020145; AC05-00OR22725; W911NF-17-1-0462; 51802280

OSTI ID:: 1787247

Alternate ID(s):: OSTI ID: 1818745

Journal Information:: npj Computational Materials, Vol. 6, Issue 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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