R&D: High-Throughput Phase-Field Simulations and ML of Resistive Switching in RRAM

npj Computational Materials has published an article written by Kena Zhang, Department of Materials Science and Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA, Jianjun Wang, Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA, Yuhui Huang, Laboratory of Dielectric Materials, State Key Laboratory of Silicon Materials, Cyrus Tang Center for Sensor Materials and Applications, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China, Long-Qing Chen, Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA, P. Ganesh, Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA, and Ye Cao, Department of Materials Science and Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA.

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 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.“