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. They reveal complex behaviors such as chirality, electronic conduction, or localized modulation of mechanical response. However, due to the different nature of the characterization methods, only limited and indirect correlative imaging has been achieved, even when probing the same spatial areas.
Researchers have developed a fast and unbiased correlative imaging analysis method for heterogeneous spatial data sets. It enables quantitative correlative multi-technique studies of functional materials. The technique allows for a precise mesoscale analysis based on a combination of data stacking, distortion correction, and machine learning.
The researchers applied the correlative imaging method to a data set containing scanning probe microscopy piezoresponse and second harmonic generation polarimetry measurements. It revealed behaviors not seen by usual manual analysis, and the origin of which is only explainable by using the quantitative correlation between the two data sets.