Optimizing Atomic Layer Deposition Using a Hybrid of Machine Learning Methods

Atomic layer deposition (ALD) is a crucial technique in semiconductor miniaturization and high-precision applications. The quality of ALD processes directly affects the properties of the resulting thin films, leading to extensive evaluations for new ALD procedures. This study uses machine learning to quickly assess ALD process quality, with a focus on predicting the standard deviations of film thickness as an indicator of quality. Using a synthetic dataset simulating non-ideal ALD processes, we evaluated the performance of Random Forest Classifier (RFC), Support Vector Machines (SVMs), and K-Nearest Neighbor (KNN). We also introduced artificial neural network (ANN) and convolutional neural network (CNN) models for predicting standard deviations of film thickness from ALD trials. Our ANN and CNN models showed promising results, positioning them as reliable tools for predicting ALD process quality.