关键词：
Spectroscopic ellipsometry
摘要：
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 (psi, & UDelta;) 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 & PLUSMN;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 & PLUSMN;1.5 nm remains remarkably robust even after 90% of the (psi, & UDelta;) are randomly eliminated. Finally, by considering (psi, & UDelta;) 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.
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