Using Machine Learning Potentials to Explore Interdiffusion at Metal-Chalcogenide Interfaces.

Chalcogenide alloys are key materials for selector and memory elements used in next-generation nonvolatile memory cells. However, the high electric fields and Joule heating experienced during operation can promote interdiffusion at the interfaces that degrade device performance over time. A clear atomic scale understanding of how chalcogenide alloys interact with electrodes could aid in identifying ways to improve long-term device endurance. In this work, we develop a robust set of moment tensor potentials (MTPs) to examine interactions between Ge-Se alloys and Ti electrodes. Previous works have shown evidence of strong interactions between Ti and chalcogenide alloys. This system offers an important first test in the use of ML empirical potentials to understand the role of interfaces in endurance in memory elements and broader nanoscale devices. The empirical potentials are constructed using an active learning moment tensor potential framework that leverages a broad data set of first-principles calculations for Ti, Ge, and Se compounds. Long-term simulations (>1 ns) show significant interdiffusion at the Ti|Ge-Se interface with Ti and Se both actively moving across the original interface. The strong chemical affinity of Ti and Se leads to a well-defined Ti-Se region and a severely Se-depleted central Ge-Se region with unfavorable selector characteristics. The evolution of the Ti-Se layer can be described using a self-limited growth model. By comparing effective Ti-Se diffusion constants for simulations at different temperatures, we find a low activation energy of 0.1 eV for Ti-Se layer interdiffusion.