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    Development and Characterization of a Dispersion-Encoded Method for Low-Coherence Interferometry

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    Author(s)
    Taudt, Christopher
    Language
    English
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    Abstract
    This Open Access book discusses an extension to low-coherence interferometry by dispersion-encoding. The approach is theoretically designed and implemented for applications such as surface profilometry, polymeric cross-linking estimation and the determination of thin-film layer thicknesses. During a characterization, it was shown that an axial measurement range of 79.91 µm with an axial resolution of 0.1 nm is achievable. Simultaneously, profiles of up to 1.5 mm in length were obtained in a scan-free manner. This marked a significant improvement in relation to the state-of-the-art in terms of dynamic range. Also, the axial and lateral measurement range were decoupled partially while functional parameters such as surface roughness were estimated. The characterization of the degree of polymeric cross-linking was performed as a function of the refractive index. It was acquired in a spatially-resolved manner with a resolution of 3.36 x 10-5. This was achieved by the development of a novel mathematical analysis approach.
    URI
    https://library.oapen.org/handle/20.500.12657/51931
    Keywords
    surface metrology; profilometry; interferometry; low-coherence interferometry; semiconductor manufacturing; optical metrology; Open Access
    DOI
    10.1007/978-3-658-35926-3
    ISBN
    9783658359263, 9783658359263
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Bern, 2022
    Imprint
    Springer Vieweg
    Classification
    Optical physics
    Scientific standards, measurement etc
    Pages
    163
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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