Network behavior in thin film growth dynamics

We present a network modeling approach for various thin film growth techniques that incorporates reemitted particles due to the nonunity sticking coefficients. We model re-emission of a particle from one surface site to another one as a network link and generate a network model corresponding to the thin film growth. Monte Carlo simulations are used to grow films and dynamically track the trajectories of re-emitted particles. We performed simulations for normal incidence, oblique angle, and chemical vapor deposition CVD techniques. Each deposition method leads to a different dynamic evolution of surface morphology due to different sticking coefficients involved and different strength of shadowing effect originating from the obliquely incident particles. Traditional dynamic-scaling analysis on surface morphology cannot point to any universal behavior. On the other hand, our detailed network analysis reveals that there exist universal behaviors in degree distributions, weighted average degree versus degree, and distance distributions independent of the sticking coefficient used and sometimes even independent of the growth technique. We also observe that network traffic during high-sticking coefficient CVD and oblique-angle deposition occurs mainly among edges of the columnar structures formed while it is more uniform and short range among hills and valleys of small sticking coefficient CVD and normal-angle depositions that produce smoother surfaces.