Abstract
Variability in resistive random access memory cell has been one of the critical challenges for the development of high-density RRAM arrays. While the sources of variability during resistive switching vary for different transition metal oxide films, the stochastic oxygen vacancy generation/recombination is generally believed to be the dominant cause. Through analyzing experimental data, a stochastic model which links the subsequent switching characteristics with its initial states of contact RRAM cells is established. By combining a conduction network model and the trap-assisted tunneling mechanism, the impacts of concentration and distribution of intrinsic oxygen vacancies in RRAM dielectric film are demonstrated with Monte Carlo Simulation. The measurement data on contact RRAM arrays agree well with characteristics projected by the model based on the presence of randomly distributed intrinsic vacancies. A strong correlation between forming characteristics and initial states is verified, which links forming behaviors to preforming oxygen vacancies. This study provides a comprehensive understanding of variability sources in contact RRAM devices and a reset training scheme to reduce the variability behavior in the subsequent RRAM states.