Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach

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Date

2018-11-02

Authors

Chaffart, Donovan
Ricardez-Sandoval, Luis A.

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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.

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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

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