EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling
This work addresses the need for automated ellipsometry modeling in materials science, though it is incremental as it builds on existing deep learning approaches.
The authors tackled the time-consuming inverse problem of ellipsometry for measuring thin film properties by introducing a large-scale benchmark dataset and a deep learning framework, achieving state-of-the-art performance compared to traditional machine learning methods.
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.