Machine learning approach to thickness prediction from in situ spectroscopic ellipsometry data for atomic layer deposition processes

Abstract

A machine learning approach is applied to estimate film thickness from in situ spectroscopic ellipsometry data. Using the atomic layer deposition of ZnO as a model process, the ellipsometry spectra obtained contains polarization data (Ψ, Δ) as a function of wavelength. Within this dataset, 95% is used for training the machine learning algorithm, and 5% is used for thickness prediction. Five algorithms—logistic regression, support vector machine, decision tree, random forest, and k-nearest neighbors—are tested. Out of these, the k-nearest neighbor performs the best with an average thickness prediction accuracy of 88.7% to within ±1.5 nm. The prediction accuracy is found to be a function of ZnO thickness and degrades as the thickness increases. The average prediction accuracy to within ±1.5 nm remains remarkably robust even after 90% of the (Ψ, Δ) are randomly eliminated. Finally, by considering (Ψ, Δ) in a limited spectral range (271–741 nm), prediction accuracies approaching that obtained from the analysis of full spectra (271–1688 nm) can be realized. These results highlight the ability of machine learning algorithms, specifically the k-nearest neighbor, to successfully train and predict thickness from spectroscopic ellipsometry data.