Understanding Defect-Mediated Ion Migration in Semiconductors using Atomistic Simulations and Machine Learning

Abstract

Ion migration in semiconductor devices is facilitated by the presence of point defects and has a major influence on electronic and optical properties. It is important to understand and identify ways to mitigate photoinduced and electrically induced defect-mediated ion migration in semiconductors. In this Perspective, we discuss the fundamental mechanisms of defect-mediated ion migration and diffusion as understood through atomistic simulations. The discussion covers a variety of case studies from the literature, with a special focus on metal halide perovskites, important materials for solar absorption and related optoelectronic applications. Tuning the perovskite composition and dimensionality and applying systematic strains are identified as ways to suppress phase segregation and ion migration. This Perspective delves into first-principles modeling approaches for defect migration and diffusion, presenting detailed case studies on the diffusion of defects and dopants in CdTe, hydrogen impurities in halide perovskites, and halogen migration in hybrid perovskites and emphasizing the importance of organic cations. The discussion further extends to accelerating the prediction of migration pathways and barriers through machine learning approaches, particularly the application of crystal-graph neural networks. By combining theoretical insights with practical case studies, this Perspective aims to provide an understanding of defect-mediated ion migration and suggestions for next-generation semiconductor discovery while considering ion migration suppression as one of many design objectives.