Atomistic insights into predictive in silico chemical vapor deposition

Abstract

An unmatched atomistic technique for predictive in silico chemical vapor deposition (CVD) is reported from an experimental and modeling perspective in the current manuscript. The gas-phase and surface-phase chemical reaction rates dependent on the precursor's flow rates, reactor chamber geometry, and other input conditions are observed to play significant roles in deciding the monolayer growth morphology. The randomness-based kinetic Monte Carlo (kMC) technique is exploited for the computation of adsorption, diffusion, and desorption rates. The deposition of silicon (Si) and silicon-germanium (Si_xGe_1−x) layers over the 〈100〉 Si substrate is carried out to measure the thin film quality, growth rate, strain, lattice constant, and mole-fraction and map the point defects (vacancies) along with the surface roughness. The impact of variation of various input physical parameters is studied during the validation of experimental results. The growth rates of Si and Si_xGe_1−x are found to be in good agreement with the experimental results under the similar input conditions. The common natural phenomenon of self-organization of spontaneously formed spatial, temporal, or spatio-temporal patterns is observed during growth through the proposed technique.