The morphological evolution of the conducting filament (CF) predominantly controls the electric response of the resistive random access memory (ReRAM) devices. However, the parameters—in terms of the material and the processing—which control the growth of such CF are plenty. Extending the phase field technique for ReRAM systems presented by Roy and Cha [J. Appl. Phys. 128, 205102 (2020)], we could successfully model the complete SET (to attain low resistance state) and RESET (to attain high resistance state) processes due to the application of sweeping voltage. The key parameters that influence the stability of the multi-cycle I-V response or the endurance behavior are identified. The computational findings of the presented model ReRAM system are practical in correlating the multi-parametric influence with the stability, variability, and reliability of the endurance cycle that affect the device performance and also lead to the device failure. We believe that our computational approach of connecting the morphological changes of the CF with the electrical response has the potential to further understand and optimize the performance of the ReRAM devices.

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The morphological evolution of the conducting filament (CF) predominantly controls the electric response of the resistive random access memory (ReRAM) devices. However, the parameters -in terms of the material and the processingwhich control the growth of such CF are plenty. Extending the phase field technique for ReRAM systems presented by Roy and Cha [J. Appl. Phys. 128, 205102 (2020)], we could successfully model the complete SET (low resistance state) and RESET (high resistance state) sates due to the application of sweeping voltage. The key parameters that influence the stability of the multi-cycle I-V response or the endurance behavior are identified. The computational findings of the presented model ReRAM system are practical in correlating the multi-parametric influence with the stability, variability, and reliability of the endurance cycle that affect the device performance and also lead to the device failure. We believe that our computational approach of connecting the morphological changes of the CF with the electrical response, has the potential to further understand and optimize the performance of the ReRAM devices.