High-Speed Piezoresponse Force Microscopy and Machine Learning Approaches for Dynamic Domain Growth in Ferroelectric Materials.

Abstract

Domain dynamics has been one of the hottest research topics for ferroelectric materials in order to understand the ferroelectric mechanisms and to develop the related applications. By using high-speed piezoresponse force microscopy (HSPFM), it is possible to observe the dynamic domain evolution in an ultrashort time increment. This paper combines the HSPFM experiments and machine learning to study the domain growth under a weak AC field in ferroelectric materials. Here, the Bayesian optimized support vector machine is employed to classify the switching domain and stable domain. The results indicate that the machine learning classifier is capable of discerning the switching area. In addition, the domain associated characteristics, such as domain pinning and domain wall pinning, can also be observed and analyzed by combining experiments and machine learning. The machine learning approach can fast and deeply extract the complicated features related to free energy from the multidimensional signals obtained by HSPFM.