Summary of Ellipbench: a Large-scale Benchmark For Machine-learning Based Ellipsometry Modeling, by Yiming Ma et al.
EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling
by Yiming Ma, Xinjie Li, Xin Sun, Zhiyong Wang, Lionel Z. Wang
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a deep learning approach to solve the inverse problem of ellipsometry, which measures optical properties and thickness of thin films. Traditional machine learning methods are used to model the complex mathematical fitting process, but this work introduces a large-scale benchmark dataset with 98 types of thin film materials and 4 substrate materials. The proposed framework leverages residual connections and self-attention mechanisms to learn massive data points, and addresses the challenge of multiple solutions in thickness prediction using a reconstruction loss. Compared to traditional methods, the framework achieves state-of-the-art performance on the proposed dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses computers to help figure out how thick some really thin materials are. Right now, people have to do this work by hand, which takes a long time. The researchers created a big collection of examples that they can use to teach a computer program how to do this job better. They also came up with a new way for the computer program to learn from all these examples and figure out what makes each material thick or thin. This new approach worked really well, beating what other computers were able to do. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Self attention
