A data-driven cross-scale polarization states recognition method based on scanning convergent beam electron diffraction in ferroelectric ceramic

Ferroelectric materials showing piezoelectricity, pyroelectricity and other functional properties have been found a variety of applications in electrical and electronic devices. These properties highly rely on polarization states in multi-scale structures, including ferroelectric domains mainly in mesoscopic scale, domain walls in microscopic scale and so on. However, it is still lack of effective method to characterize multi-scale polarization states simultaneously in ferroelectric materials. Here, we proposed a data-driven cross-scale polarization state recognition method based on scanning convergent beam electron diffraction (SCBED) to characterize the complicated polarization states in a PbZr_0.4Ti_0.6O₃ ceramic. This method employed a deep learning model to interpret the extensive dataset of CBED patterns generated during the scanning process and further validated by atomic resolution transmission electron microscope (ARTEM). The data-driven SCBED method provided a novel strategy for characterizing and interpreting the complicated cross-scale structure frame in ferroelectric materials.