Artificial Intelligence in Ellipsometry: Methods, Challenges, and Opportunities

Spectroscopic ellipsometry (SE) is a non-destructive optical technique widely used to extract film thickness, optical constants, surface roughness, and compositional information. Despite its versatility and high sensitivity, conventional SE analysis typically relies on iterative nonlinear regression of parameterized optical models, which is computationally demanding, strongly dependent on expert intervention, and often affected by convergence and non-uniqueness issues. In recent years, artificial intelligence (AI) has emerged as a promising route to overcome these limitations. This review summarizes recent advances in AI-driven ellipsometry, spanning traditional machine learning (ML) methods, such as artificial neural networks (ANNs), support vector regression (SVR), and support vector machines (SVMs), as well as deep learning (DL) architectures, including convolutional neural networks (CNNs). Representative applications in semiconductors, perovskites, and biosensing are also discussed. Overall, this review highlights the opportunities and current limitations of AI for next-generation ellipsometric characterization.