Modeling and control of Transparent Conducting Oxide layer surface morphology for improved light trapping

Abstract

This work first introduces a kinetic Monte–Carlo simulation model for a two species thin film deposition process and demonstrates the use of feedback control, coupled with a suitable actuator design, in manufacturing thin films whose surface morphology has a structure that improves light trapping. This work is relevant in the context of a Transparent Conducting Oxide (TCO) thin film layer manufacturing used in thin film solar cells where it is desirable to produce thin films with precisely tailored surface morphology. Specifically, a two species thin film deposition process involving atom adsorption, surface relaxation and surface migration is initially considered and is modeled using a large-lattice (lattice size=40,000) kinetic Monte–Carlo simulation. Subsequently, thin film surface morphology characteristics like roughness and slope are computed with respect to different characteristic length scales ranging from atomic to the ones corresponding to visible light wavelength and it is found that a patterned actuator design is needed to induce thin film surface roughness and slope at visible light wavelength spatial scales, that lead to desired thin film solar cell performance. Then, an Edwards–Wilkinson type equation is used to model the surface evolution at the visible light wavelength spatial scale and form the basis for the design of a predictive feedback controller whose objective is to manipulate the deposition rate across the spatial domain of the process. The model parameters of the Edwards–Wilkinson equation are estimated from kinetic Monte–Carlo simulations and their dependence on the deposition rate is used in the formulation of the predictive controller to predict the influence of the control action on the surface roughness and slope throughout the thin film growth process. Analytical solutions of the expected surface roughness and surface slope at the visible light wavelength spatial scale are obtained by solving the Edwards–Wilkinson equation and are used in the predictive controller formulation and in the control action calculation. The controller is applied to the large-lattice kinetic Monte–Carlo simulation. Simulation results demonstrate that the proposed controller and patterned actuator design successfully regulate aggregate surface roughness and slope to set-point values at the end of the deposition.