Correlative imaging of ferroelectric domain walls

Abstract

The wealth of properties in functional materials at the nanoscale has attracted tremendous interest over the last decades, spurring the development of ever more precise and ingenious characterization techniques. In ferroelectrics, for instance, scanning probe microscopy based techniques have been used in conjunction with advanced optical methods to probe the structure and properties of nanoscale domain walls, revealing complex behaviours such as chirality, electronic conduction or localised modulation of mechanical response. However, due to the different nature of the characterization methods, only limited and indirect correlation has been achieved between them, even when the same spatial areas were probed. Here, we propose a fast and unbiased analysis method for heterogeneous spatial data sets, enabling quantitative correlative multi-technique studies of functional materials. The method, based on a combination of data stacking, distortion correction, and machine learning, enables a precise mesoscale analysis. When applied to a data set containing scanning probe microscopy piezoresponse and second harmonic generation polarimetry measurements, our workflow reveals behaviours that could not be seen by usual manual analysis, and the origin of which is only explainable by using the quantitative correlation between the two data sets.

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

“…[12] Similarly, machine learning methods have boosted the sensitivity of second-harmonic generation microscopy (SHG) to obtain phase information in reference-free experiments, [13] provide automatic cancer diagnosis in breast tissues using high-throughput SHG polarimetry studies, [14] or achieve correlative imaging at the nanoscale by combining SHG polarimetry and scanning probe microscopy. [15] This study demonstrates the high potential of laterally-resolved SHG polarimetry assisted by machine learning methods to investigate the ferroic order through the analysis of the domain structure of Germanium Telluride (GeTe), a high-T c non-oxide ferroelectric. Such Rashba-type ferroelectrics have recently attracted great attention owing to their potential technological applications based on the ferroelectric control of the Rashba-type spin-orbit coupling.…”

“…In particular, clustering methods are highly suited for the identification of groups with distinct properties in a given data set, based on a concept of similarity between elements within each cluster. [15,30,46,47] In the present analysis, clustering was performed through K-means clustering in order to segment the SHG datasets into regions of interest with distinct behaviors corresponding to ferroelectric domain variants. Euclidean distance criterion is used to segment the data set into spatially indexed clusters, with centroids encoding the differing mean behaviors within each cluster.…”

“…[ 12 ] Similarly, machine learning methods have boosted the sensitivity of second‐harmonic generation microscopy (SHG) to obtain phase information in reference‐free experiments, [ 13 ] provide automatic cancer diagnosis in breast tissues using high‐throughput SHG polarimetry studies, [ 14 ] or achieve correlative imaging at the nanoscale by combining SHG polarimetry and scanning probe microscopy. [ 15 ]…”

Center‐Type Topological Domain States in Ferroelectric Nanodots Tailored from Thin Films

“…[25] Thus, in the last two decades, PFM has become an indispensable tool in observing ferroelectric properties owing to its ease of operation and nanoscale resolution. [26][27][28][29][30][31] Many studies on bulk ferroelectrics seek to determine their 3D domain structure through the domain control of local surfaces and imaging of the polarization distribution. In the case of nanoscale ferroelectrics, the growth and structure of exotic domains have been observed in BaTiO 3 , Pb(Zr 0.2 Ti 0.8 )O 3 , and BiFeO 3 films and nanodot arrays.…”

Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors

“…To address these limitations, machine learning (ML) has proven itself a valuable tool for facilitating interpretation of multidimensional datasets, including those generated by resonant SPM methods like RPFM. [27,[31][32][33][34][35][36][37][38] Although less susceptible to user bias, ML approaches are still mathematical algorithms by definition, agnostic of the underlying physical and chemical phenomena. Hence, their outputs are not necessarily representative of the physical response of the sample.…”