Identification and Evaluation of a Dynamic Model for a Thin Film Deposition Process

This study proposes an algorithm for computing a dynamic model for a thin film deposition process. The proposed algorithm is used on high dimensional kinetic Monte Carlo (KMC) simulations and consists of applying principal component analysis (PCA) for reducing the state dimension, self organizing map (SOM) for grouping similar surface configurations and simple cell mapping (SCM) for identifying the transitions between different surface configuration groups. The error associated with this model reduction approach is characterized by running 600 test simulations with highly dynamic and random input profiles. Global error, which is the normalized Euclidean distance between the real and predicted states, is found out to be 0.0058 on average for the test simulations. Our study shows that the proposed algorithm is useful for extracting dynamic models from high dimensional and noisy molecular simulation data.