Correlative imaging of ferroelectric domain walls

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.

    Organizational unit
    Paruch Group
    Type
    Dataset
    DOI
    10.26037/yareta:cpcnmarfcvhnlf4ijcfnuvmm2e
    License
    Creative Commons Attribution 4.0 International
Publication date28/05/2021
Retention date26/05/2031
accessLevelPublicAccess levelPublic
SensitivityUndefined
licenseContract on the use of data
License
Contributors
  • Gaponenko, Iaroslav orcid
  • Cherifi-Hertel, Salia
  • Acevedo-Salas, Ulises
  • Bassiri-Gharb, Nazanin
  • Paruch, Patrycja
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