Pre-production estimates for the choice of strategy and algorithm for monochromatic monitoring of the production of optical coatings
The paper proposes an approach for the pre-production selection of the most promising combination of a monochromatic monitoring strategy and a monitoring algorithm used for predicting termination of layer deposition. The approach is based on the previously introduced estimate of the strength of the...
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| Vydáno v: | Applied optics. Optical technology and biomedical optics Ročník 62; číslo 18; s. 4906 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
20.06.2023
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| ISSN: | 1539-4522, 1539-4522 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
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| Shrnutí: | The paper proposes an approach for the pre-production selection of the most promising combination of a monochromatic monitoring strategy and a monitoring algorithm used for predicting termination of layer deposition. The approach is based on the previously introduced estimate of the strength of the expected error self-compensation effect and the new estimate, to the best of our knowledge, of the strength of the cumulative effect of thickness error growth presented in this paper. The application of the proposed approach is demonstrated using computational manufacturing experiments with various types of optical coating designs.The paper proposes an approach for the pre-production selection of the most promising combination of a monochromatic monitoring strategy and a monitoring algorithm used for predicting termination of layer deposition. The approach is based on the previously introduced estimate of the strength of the expected error self-compensation effect and the new estimate, to the best of our knowledge, of the strength of the cumulative effect of thickness error growth presented in this paper. The application of the proposed approach is demonstrated using computational manufacturing experiments with various types of optical coating designs. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1539-4522 1539-4522 |
| DOI: | 10.1364/AO.493497 |