Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks

TL;DR

This paper demonstrates that deep neural networks can accurately predict saturation times in atomic layer deposition processes from reactor data, reducing experimental effort for process optimization.

Contribution

It introduces a neural network-based method for predicting saturation times in ALD, tailored to specific reactor geometries, enhancing process optimization efficiency.

Findings

Neural networks accurately predict saturation times.

The approach reduces the need for extensive experiments.

Prediction accuracy depends on reactor geometry.

Abstract

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.