Probing polarization dynamics at specific domain configurations: Computer-vision based automated experiment in piezoresponse force microscopy

Abstract

Topological defects in ferroelectric materials have attracted much attention due to the emergence of conductive, ferroic, and magnetic functionalities. However, many topological configurations dynamically evolve during the switching processes, making them a challenge to characterize via traditional techniques. Here, we implement an automated experimentation approach for the exploration of functional properties in BiFeO3 thin films. Specifically, we visualize the ferroelectric domain structures via single frequency piezoresponse force microscopy and implement a computer vision-based algorithm to discover features of interest at which spectroscopic measurements are taken. Subsequently, we employ dimensionality reduction techniques to reveal characteristic polarization behaviors at these features. This approach can be extended to other spectroscopies and modalities to probe only specific features of interest, ultimately enabling dynamical processes in ferroelectrics to be studied.

Automated Experiments of Local Non‐Linear Behavior in Ferroelectric Materials

“…More importantly, this approach equally samples all the correlations present in the data and the regions with interesting behaviors can remain unnoticed; consequently, the relevant physical mechanisms can potentially be missed. Alternatively, Kelley, [69] Liu, [70] and Volpe [71] have shown that in combination, computer vision and automated experiments can select locations for in-depth spectroscopic studies based on a priori criteria in SPM. However, this approach relies on the a priori known objects of interest, rather than the discovery of microstructural elements having specific behaviors.…”

Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films

“…A variety of BE modes have been developed by changing the transformation bias. For example, bipolar triangular waveforms can be used to measure ferroelectric switching (Figures S1 and S2, Supporting Information), [15] First-order reversal curves can measure switching dynamics, [16] and contact-Kelvin probe force microscopy to isolate ionic, electrostatic, ferroelectric contributions. [17][18][19] The challenge is extracting actionable information from data that spans many positions, frequency, voltage, cycle, and time dimensions and contains multichannel information.…”

Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy

“…Measurements over a dense grid can be time-consuming, are associated with accumulated damage to the AFM tip and surface, and are not guaranteed to sample the object of interest such as grain boundary (GB) if the spatial density of the latter is low. Previously, we have introduced a direct computer vision-based method where we use a pretrained supervised network , to identify objects of interest and explore their transport properties . Here, we implement the solution of the opposite problemnamely, discover what region of the surface and microstructural element are responsible for a specific aspect of transport behavior.…”