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Noninvasive material anisotropy estimation using oblique incidence reflectometry and machine learning

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

DOI

  • Lezhong Wang, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark
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  • Siavash Arjomand Bigdeli, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark
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  • Anders Nymark Christensen, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark
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  • Milena Corredig
  • Riccardo Tonello, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark
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  • Anders Bjorholm Dahl, Danmarks Tekniske Universitet, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark, Danmark
  • Jeppe Revall Frisvad, Danmarks Tekniske Universitet, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark, Danmark

Anisotropy reveals interesting details of the subsurface structure of a material. We aim at noninvasive assessment of material anisotropy using as few measurements as possible. To this end, we evaluate different methods for detecting anisotropy when observing (1) several sample rotations, (2) two perpendicular planes of incidence, and (3) just one observation. We estimate anisotropy by fitting ellipses to diffuse reflectance isocontours, and we assess the robustness of this method as we reduce the number of observations. In addition, to support the validity of our ellipse fitting method, we propose a machine learning model for estimating material anisotropy.

OriginalsprogEngelsk
Artikelnummer5
TidsskriftOptical Materials Express
Vol/bind13
Nummer5
Sider (fra-til)1457-1474
Antal sider18
ISSN2159-3930
DOI
StatusUdgivet - 27 apr. 2023

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