  Ellipsometry is used to indirectly measure the optical properties and
thickness of thin films. However, solving the inverse problem of ellipsometry
is time-consuming since it involves human expertise to apply the data fitting
techniques. Many studies use traditional machine learning-based methods to
model the complex mathematical fitting process. In our work, we approach this
problem from a deep learning perspective. First, we introduce a large-scale
benchmark dataset to facilitate deep learning methods. The proposed dataset
encompasses 98 types of thin film materials and 4 types of substrate materials,
including metals, alloys, compounds, and polymers, among others. Additionally,
we propose a deep learning framework that leverages residual connections and
self-attention mechanisms to learn the massive data points. We also introduce a
reconstruction loss to address the common challenge of multiple solutions in
thin film thickness prediction. Compared to traditional machine learning
methods, our framework achieves state-of-the-art (SOTA) performance on our
proposed dataset. The dataset and code will be available upon acceptance.
