Characterization and dynamics of Si self-interstitial clusters by self-learning kinetic Monte Carlo simulations

Abstract

Si is a semiconductor material whose relevance in the industry is undeniable, being implemented in every generation of the transistor scaling over the last decades thanks to their excellent properties and easy production. During the fabrication process it is common to deal with the diffusion of impurity atoms in Si, which is critically influenced by intrinsic defects such as self-interstitials and vacancies. Point defects tend to aggregate forming small clusters and extended defects and therefore, the dopant diffusivity is enhanced and leakage currents are increased in the final device. The aim of this work is to study the energetic characteristics of small Si interstitials clusters from an atomistic point of view, determining their formation enthalpies and energy barriers for each cluster size. To do so, we have run simulations with the kinetic Activation-Relaxation Technique. We have characterized each geometrical configuration based on energetic and visual criteria, classifying the small clusters in chainlike, compact or (111) configurations. The transition barriers between these structures have been also determined. This information is useful to understand the behaviour of small clusters in crystal and how they can evolve to extended defects. This study can also be applied to other semiconductor materials.