Bibliographic Details
| Title: |
Machine learning powered ellipsometry |
| Authors: |
Jinchao Liu, Di Zhang, Dianqiang Yu, Mengxin Ren, Jingjun Xu |
| Source: |
Light: Science & Applications, Vol 10, Iss 1, Pp 1-7 (2021) |
| Publisher Information: |
Nature Publishing Group, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Applied optics. Photonics
LCC:Optics. Light |
| Subject Terms: |
Applied optics. Photonics, TA1501-1820, Optics. Light, QC350-467 |
| More Details: |
Abstract Ellipsometry is a powerful method for determining both the optical constants and thickness of thin films. For decades, solutions to ill-posed inverse ellipsometric problems require substantial human–expert intervention and have become essentially human-in-the-loop trial-and-error processes that are not only tedious and time-consuming but also limit the applicability of ellipsometry. Here, we demonstrate a machine learning based approach for solving ellipsometric problems in an unambiguous and fully automatic manner while showing superior performance. The proposed approach is experimentally validated by using a broad range of films covering categories of metals, semiconductors, and dielectrics. This method is compatible with existing ellipsometers and paves the way for realizing the automatic, rapid, high-throughput optical characterization of films. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2047-7538 |
| Relation: |
https://doaj.org/toc/2047-7538 |
| DOI: |
10.1038/s41377-021-00482-0 |
| Access URL: |
https://doaj.org/article/e5e85f63cafd41e6b1a61bb04daffc85 |
| Accession Number: |
edsdoj.5e85f63cafd41e6b1a61bb04daffc85 |
| Database: |
Directory of Open Access Journals |
