Neural network-based model predictive control for thin-film chemical deposition of quantum dots using data from a multiscale simulation

Abstract

Recently, thin-film deposition of quantum dot (QDs) to manufacture solar cells and displays have received significant attention due to the lucrative optoelectronic properties of these devices. Unfortunately, (a) the existing macroscopic thin-film deposition models in the literature do not consider the surface-level interactions; (b) the detailed surface-level models do not consider the entire thin-film deposition ensemble; and (c) multiscale modeling studies considering both the scales are not tailored for QD systems. Thus, to address this knowledge gap, in this work, a multiscale thin-film deposition model is developed. First, the droplet distribution and evaporation dynamics during the thin-film deposition of QDs are described using heat and mass balance equations. Second, a microscopic discrete-element method (DEM)-based particle aggregation model that describes the surface-level particle interactions is developed and combined with the macroscopic dynamics. Furthermore, a model predictive controller (MPC) is designed to regulate the film thickness and minimize the film roughness by manipulating key process variables. To design a feasible MPC, a computationally efficient artificial neural network (ANN) model of the thin-film deposition model is constructed, and it is incorporated within the MPC. The closed-loop simulation results showcase the capability of the MPC to achieve the required film thickness and minimize the roughness.