260872 Microscopic Modeling of a Two-Species Thin Film Deposition Process Arising in Transparent Conducting Oxide Layer Manufacturing

Thursday, November 1, 2012: 8:30 AM
327 (Convention Center )
Jianqiao Huang1, Gerassimos Orkoulas2 and Panagiotis D. Christofides1, (1)Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, (2)Chemical Engineering, University of California, Los Angeles, Los Angeles, CA

Thin film solar cells constitute an important and growing component of the overall solar cell market owing to their potential of
yielding improved light conversion efficiencies. The Transparent Conducting Oxide (TCO) layer, which typically consists of zinc oxide (ZnO) and aluminum (Al), is an important component of thin film solar cells and has a crucial influence on the performance of thin film solar cell systems. In addition to investigating the performance with respect to light conversion efficiency and long-term stability of an array of materials, thin film solar cell technology stands to benefit from fundamental microscopic modeling and optimal thin film manufacturing (deposition) control strategies that produce thin films with desired light trapping properties. This work focuses on the modeling of a large-scale two-species thin film deposition process and its application in the manufacturing process of TCO layers.
Aggregate surface roughness and slope are introduced to characterize the properties of TCO thin film surface properties. Both kinetic Monte Carlo (kMC) models and stochastic partial differential equation (SPDE) models are introduced to describe the film surface morphology dynamics. In the kMC models, a solid-on-solid square lattice is used to simulate the process and different growth mechanisms are utilized for each component, zinc oxide (ZnO) and aluminum (Al). Specifically, a deposition/migration mechanism is used for ZnO and a random deposition with surface relaxation mechanism is used for Al. In SPDE models, an Edwards-Wilkinson type equation is used to predict the process dynamics, which can be used in future work to design a feedback controller. Extensive simulation results will be presented to elucidate the dynamic behavior of surface morphology and pave the way for model-based feedback control.

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