Improvements of birefringence imaging techniques to observe stress-induced ferroelectricity in SrTiO3 based on K-means clustering with circular statistics - Kensei Toyoda; Hirotaka Manaka; Yoko Miura (Science and Technology of Advanced Materials: Methods)
Optical birefringence imaging techniques have enabled quantitative evaluation of macroscopic structures, e.g. domains and grain boundaries. With inhomogeneous samples, the selection of regions for analysis can significantly affect the conclusions; thus, arbitrary selection can lead to inaccurate findings. Thus, in this study, we present a method to cluster all birefringence imaging data using K-means multivariate clustering on a pixel-by-pixel basis to eliminate arbitrariness in the region selection process. Linear statistics cannot be applied to the polarization states of light described by angles and their periodicity; thus, circular statistics are used for clustering. By applying this approach to a 42,280-pixel image comprising 12 explanatory variables of stress-induced ferroelectricity in SrTiO3, we were able to select a region of locally developed spontaneous polarization. This region covers only 1.9% of the total area, where the stress and/or strain is concentrated, thereby resulting in a higher ferroelectric phase transition temperature and larger spontaneous polarization than in the other regions. The K-means multivariate clustering with circular statistics is shown to be a powerful tool to eliminate arbitrariness. The proposed method is a significant analysis technique that can be applied to images using the polarization of light, azimuthal angle of crystals, scattering angle.