Artificial Neural Network Discrimination for Parameter Estimation and Optimal Product Design of Thin Films Manufactured by Chemical Vapor Deposition

Industrial production of valuable chemical products often involves the manipulation of phenomena evolving at different temporal and spatial scales. Product properties can be captured accurately using computationally expensive stochastic multiscale models that explicitly consider the feedbacks between different scales. However, product design quality is often tampered by uncertainties affecting process operation. In this work, we used artificial neural networks (ANNs) to estimate an uncertain parameter, accurately predict product properties under uncertainty, and achieve orders-of-magnitude computational savings of a multiscale model of thin film formation by chemical vapor deposition. ANNs were trained using multiple realizations of the uncertain parameter to capture the behavior of the thin film's two key microscale properties: roughness and growth rate. Next, mean square error and maximum likelihood estimation were used for parameter estimation and to find the ANN that could generate the closest predictions to the real-time measurements collected from the process in the presence of uncertainty. The chosen ANNs were employed to seek for the optimal operating conditions to enable the fabrication process to meet product quality specifications. ANNs are a promising technique for product property prediction and efficient decision making in the design of optimal operating conditions for chemical processes under uncertainty.