Noninvasive material anisotropy estimation using oblique incidence reflectometry and machine learning

Abstract

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.