A Study of the Variability in Contact Resistive Random Access Memory by Stochastic Vacancy Model

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.

Continual Learning Electrical Conduction in Resistive‐Switching‐Memory Materials

“…For example, distribution of the low resistance level after the forming operation for 256 TiN/TiON/SiO 2 RS devices was examined using Monte Carlo simulations. [36] The simulations showed that the resistance distribution upon the forming operation agrees well with experimental data. Moreover, finite element simulations were carried out to study the distribution of forming voltage for 206 runs of HfO 2 -based RS devices with planar electrodes, and for 196 runs of HfO 2 -based RS devices with protruding electrodes.…”

A Library of Atomically Thin 2D Materials Featuring the Conductive‐Point Resistive Switching Phenomenon

“…For non-volatile resistive switching devices, variations are one of the major challenges due to the stochastic nature of the switching process. [33,34] Therefore, reducing variability for practical applications is an essential matter, which is expected to benefit from further advances in 2D material growth, device optimization, and testing protocol. Some mitigation strategies in material and interface engineering, [5,35] and programming optimization that can constrain the electrical variability [33] has proved to be effective for conventional transitional metal oxide memories.…”

Effect of Surface Variations on Resistive Switching

“…However, the formation and recombination of oxygen vacancies is highly stochastic in nature and play dominant role in deciding degree of variability. After analyzing the experimental data, Monte Carlo simulation has established a potential stochastic model that relates subsequent RS behavior to the initial states of contact in resistive memory cells [75].…”