Advanced algorithms for monochromatic monitoring of complex optical coatings

The paper proposes to use combinations of two algorithms for the on-line prediction of the termination times of layer depositions in the case of complex optical coatings with large numbers of layers with significantly different layer thicknesses. Notch filters with very thin and thick coating layers...

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Vydáno v:Applied optics. Optical technology and biomedical optics Ročník 62; číslo 30; s. 7904
Hlavní autoři: Tikhonravov, A, Lagutin, Iu, Lagutina, A, Romanov, B, Yagola, A
Médium: Journal Article
Jazyk:angličtina
Vydáno: 20.10.2023
ISSN:1559-128X, 1539-4522
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Shrnutí:The paper proposes to use combinations of two algorithms for the on-line prediction of the termination times of layer depositions in the case of complex optical coatings with large numbers of layers with significantly different layer thicknesses. Notch filters with very thin and thick coating layers are considered as examples of coatings where such combinations of algorithms can be useful. Computational manufacturing experiments are used to estimate expected levels of thickness errors and the strength of the error self-compensation effect for various notch filter designs. It is shown that the estimates obtained can be used to select the optimal combination of the considered algorithms.The paper proposes to use combinations of two algorithms for the on-line prediction of the termination times of layer depositions in the case of complex optical coatings with large numbers of layers with significantly different layer thicknesses. Notch filters with very thin and thick coating layers are considered as examples of coatings where such combinations of algorithms can be useful. Computational manufacturing experiments are used to estimate expected levels of thickness errors and the strength of the error self-compensation effect for various notch filter designs. It is shown that the estimates obtained can be used to select the optimal combination of the considered algorithms.
Bibliografie:ObjectType-Article-1
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content type line 23
ISSN:1559-128X
1539-4522
DOI:10.1364/AO.501489