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

arXiv: https://arxiv.org/abs/2205.08378 · HTML: https://mlsanity.com/paper/2205.08378/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)

## Built on by (most cited first, 2 of 2)

- [LLM Agents for Knowledge Discovery in Atomic Layer Processing](https://mlsanity.com/paper/2509.26201/) (2025)
- [Surrogate models to optimize plasma assisted atomic layer deposition in high aspect ratio features](https://mlsanity.com/paper/2506.09313/) (2025)

## Abstract (arXiv)

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.