Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
This work addresses the optimization of ALD processes for materials science and engineering, but it is incremental as it applies existing neural network methods to a specific domain with reactor-dependent datasets.
The authors tackled the problem of optimizing atomic layer deposition (ALD) processes by using deep neural networks to predict saturation times from reactor growth profiles, achieving accurate predictions without prior surface kinetics information, which reduces the number of experiments needed for optimization.
In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor. We introduce a dataset designed to train neural networks to predict saturation times based on the dose time and thickness values measured at different points of the reactor for a single experimental condition. We then explore different artificial neural network configurations, including depth (number of hidden layers) and size (number of neurons in each layers) to better understand the size and complexity that neural networks should have to achieve high predictive accuracy. The results obtained show that trained neural networks can accurately predict saturation times without requiring any prior information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor. However, the datasets and training procedure depend on the reactor geometry.