Automatic Ferroelectric Domain Pattern Recognition Based on the Analysis of Localized Nonlinear Optical Responses Assisted by Machine Learning

Second‐harmonic generation (SHG) is a nonlinear optical method allowing the study of the local structure, symmetry, and ferroic order in noncentrosymmetric materials such as ferroelectrics. The combination of SHG microscopy with local polarization analysis is particularly efficient for deriving the local polarization orientation. This, however, entails the use of tedious and time‐consuming modeling methods of nonlinear optical emission. Moreover, extracting the complex domain structures often observed in thin films requires a pixel‐by‐pixel analysis and the fitting of numerous polar plots to ascribe a polarization angle to each pixel. Here, the domain structure of GeTe films is studied using SHG polarimetry assisted by machine learning. The method is applied to two film thicknesses: A thick film containing large domains visible in SHG images, and a thin film in which the domains' size is below the SHG resolution limit. Machine learning‐assisted methods show that both samples exhibit four domain variants of the same type. This result is confirmed in the case of the thick film, both by the manual pixel‐by‐pixel analysis and by using piezoresponse force microscopy. The proposed approach foreshows new prospects for optical studies by enabling enhanced sensitivity and high throughput analysis. Nonlinear optical microscopy with polarimetry analysis supported by machine learning is employed to study complex ferroelectric domain structures down to the sub‐100 nm scale. This combination enables enhanced sensitivity and high throughput analysis. In particular, clustering methods allow for determining the domain variants by automatically detecting the main types of second harmonic polar plots (centroids) and their corresponding ferroelectric domains (clusters).