UCLA

Resistive random access memory (RRAM) is one of the emerging technologies of non-volatile memory that attracts excellent research interest. This research uses a Kinetic Monte Carlo (KMC) simulation to demonstrate the switching physics in the ???x-based Ox-RRAM, including forming, SET, and RESET processes. A dynamic resistor network has been built to simulate oxygen vacancy generation, oxygen vacancy, oxygen ion recombination, oxygen ion migration, and hopping during resistive switching. The conductive filament (CF) configuration resulting from oxygen vacancy distribution is updated in time steps. The proposed KMC model can provide insight into RRAM electronic characteristics based on its microstructural material behaviors. The switching layer was divided into resistors with different resistances. The research aims to use this model as the foundation to predict the performance and reliability of RRAM devices, which covers performance fluctuation during switching, resistance state degradation (endurance), cycle-to-cycle, device-to-device variation, retention, and window margin trade-off. This simulation model provides a tool for comparing parameter significance and predicting device behaviors. This research also explores implementing the Bayesian approach that can integrate heterogeneous information sources and modify the simulation results.