Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach
Loading...
Date
2018-11-02
Authors
Chaffart, Donovan
Ricardez-Sandoval, Luis A.
Advisor
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
This work details the construction and evaluation of a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model. The multiscale model combines continuum differential equations, which describe the transport of the precursor gas phase, with a stochastic partial differential equation (SPDE) that predicts the evolution of the thin film surface. In order to allow the SPDE to accurately predict the thin film growth over a range of system parameters, an ANN is developed and trained to predict the values of the SPDE coefficients. The fully-assembled hybrid multiscale model is validated through comparison against a kinetic Monte Carlo-based thin film multiscale model. The model is subsequently applied to a series of optimization and control studies to test its performance under different scenarios. These studies illustrate the computational efficiency of the proposed hybrid multiscale model for optimization and control applications.
Description
The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.compchemeng.2018.08.029 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
Artificial neural networks, Hybrid modelling, Multiscale modelling, Stochastic partial differential equations, Thin film deposition