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.
Fast and stable switching between states is one of the key factors for the success resistive random access memory (RRAM) development. In an array, wide reset efficiency variation in RRAM cells is found to link to the characteristics of its low frequency noise (LFN) in bit-cell current. Through Monte Carlo simulation on randomly placing conductive filaments (CF), LFN characteristics correspond to the densities of the CF in the RRAM film. Further correlations between LFN features and the reset efficiency are found. In addition, CF topography are found to change after long term cycling tests. A trimming process is proposed to minimize the impacts of stochastic CF generation, leading to increase reset speed.INDEX TERMS Variability, resistive random access memory, low frequency noise, Monte Carlo simulation, conductive filament.
As one of the most promising embedded non-volatile storage solutions for advanced CMOS modules, resistive random access memory’s (RRAM) applications depend highly on its cyclability. Through detailed analysis, links have been found between noise types, filament configurations and the occurrence of reset failure during cycling test. In addition, a recovery treatment is demonstrated to restore the cyclability of RRAM. An early detection circuit for vulnerable cells in an array is also proposed for further improving the overall endurance of an RRAM array. Lifetime of RRAM can be extended to over 10 k cycles without fail bits in an array.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.