Sparse Ellipsometry

Sparse ellipsometry aims to improve the speed and portability of ellipsometry, a technique used to measure the optical properties and thickness of thin films. Current research focuses on leveraging machine learning, particularly deep learning models (like convolutional neural networks and those incorporating residual connections and self-attention), to accelerate data analysis and reduce the need for complex, time-consuming instrumentation. This approach promises faster, more accessible characterization of thin films, impacting diverse fields such as materials science, nanotechnology, and manufacturing by enabling in-situ monitoring and analysis of film growth and properties.