Kinetic Monte Carlo Simulation of Interface-Controlled Hafnia-Based Resistive Memory

Kinetic Monte Carlo simulations of resistive memory devices have been performed by paying attention to the vacancy-interstitial generation near the Hafnia-metal electrode interface. In our model, an oxygen vacancy is generated in Hafnia near the interface, with the corresponding oxygen atom residing in the metal electrode. These oxygen atoms form a thin insulating oxide layer at the Hafnia-active electrode interface. This interfacial layer is essential to thicken the filament, even after the filament bridges the two metal electrodes at low current levels. This thickening of the conducting filament is captured by the model and it naturally explains the trend of resistance decrease with an increase in compliance current found in some experiments. Simulations results as a function of the bonding energy between vacancies show a large increase in retention time with an increase in bonding energy. We also find that as the compliance current increases, the morphology of the filament transitions from conical to dumbbell-shaped. Finally, using a single set of values for various energies, our simulations capture the SET, RESET, and retention processes.

I. Introduction

Resistive random access memory (RRAM) devices are promising candidates to replace Flash memory, and their memory switching characteristics have been demonstrated in a variety of material systems [1]–[12]. RRAM devices can be integrated in a crossbar architecture, enabling the realization of large and dense memory arrays. The switching speeds of these devices are high (less than 10-ns switching time [11]), and they exhibit long retention times (over s at 100 °C [6]). Recent work also points to the potential of using RRAM devices for neuromorphic computing [2], [9], [13], and for efficiently realizing logic operations [1], [14]–[18]. All these applications require a comprehensive physics-based understanding and a physical model that captures the dynamics of the filament formation process, including its stability and switching times between resistance levels. The model should have the ability to simulate statistically relevant device parameters such as resistance values and switching times because of the stochastic nature of filament formation.