Chaffart, DonovanRicardez-Sandoval, Luis A.2018-10-262018-10-262018-11-02https://dx.doi.org/10.1016/j.compchemeng.2018.08.029http://hdl.handle.net/10012/14071The 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/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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Artificial neural networksHybrid modellingMultiscale modellingStochastic partial differential equationsThin film depositionOptimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approachArticle