TY - JOUR
T1 - Noninvasive material anisotropy estimation using oblique incidence reflectometry and machine learning
AU - Wang, Lezhong
AU - Arjomand Bigdeli, Siavash
AU - Nymark Christensen, Anders
AU - Corredig, Milena
AU - Tonello, Riccardo
AU - Dahl, Anders Bjorholm
AU - Frisvad, Jeppe Revall
PY - 2023
Y1 - 2023
N2 - 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
AB - 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
UR - https://doi.org/10.11583/DTU.22654417.v1
UR - https://doi.org/10.11583/DTU.22654411.v1
U2 - 10.1364/OME.486542
DO - 10.1364/OME.486542
M3 - Journal article
SN - 2159-3930
VL - 13
SP - 1457
EP - 1474
JO - Optical Materials Express
JF - Optical Materials Express
IS - 5
ER -