Physical Modeling of Conductive Filament Growth and Resistive Switching Dynamics in Metal Oxide-Based RRAM

Metal oxide-based resistive random-access memory (RRAM) offers several excellent performances such as lower power consumption, fast switching speed, simple structure, and compatibility to the complementary metal oxide semiconductor (CMOS) technologies which can potentially replace the traditional memory. The functionality of a metal oxide-based RRAM stems from the conductive filament (CF) of high concentration of oxygen vacancy, which initially forms, and later grows or dissolves inside the oxide switching layer during the resistive switching process. To date, various metal oxide-based RRAMs have been reported in literatures. However, the multi-physics processes and their complicated interplays during the formation, growth and rupture of the CFs are not fully understood, which severely limits the application of RRAMs in semiconductor industry. In this study, we develop a comprehensive physical model based on defect chemistry and charge transport theory to investigate the electroforming process and subsequent resistive switching behavior, using HfO_2-<i>x</i>as a prototypical model system. Our simulation results indicate that the CF formation is assisted by the supply of oxygen vacancies at the anode/oxide interface and the oxygen vacancy transport in the bulk. The effects of the electrode properties and the intrinsic metal oxide properties on the CF growth behavior during the electroforming process are further explored. Next, the roles of the electrical bias, heat transport and Vegard strain effect, as well as the electrical and thermal properties of the switching layers in the resistive switching processes are systematically investigated. Finally, high-throughput simulations and machine learning method are performed to derive interpretable analytical models for device performance metrics in terms of key material parameters. These analytical models reveal that optimal resistive performance can be achieved in materials with a low Lorenz number. This work provides a fundamental understanding of the CF formation and growth behaviors in the electroforming and resistive switching processes, and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance.