EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling
<|reference_start|>EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling: 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.<|reference_end|>
@article{ma2024ellipbench:,
    title={EllipBench: A Large-scale Benchmark for Machine-learning based
  Ellipsometry Modeling},
    author={Yiming Ma, Xinjie Li, Xin Sun, Zhiyong Wang, Lionel Z. Wang},
    journal={arXiv preprint arXiv:2407.17869},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17869},
    primaryClass={cs.LG}
}