The wealth of properties in functional materials at the nanoscale has piqued the interest of many researchers in recent decades, fueling the development of ever more precise and inventive characterization techniques. Scanning probe microscopy-based techniques, for example, have been used in conjunction with advanced optical methods to probe the structure and properties of nanoscale domain walls in ferroelectrics. They reveal complex behaviors such as chirality, electronic conduction, and localized mechanical response modulation. However, even when probing the same spatial areas, only limited and indirect correlative imaging has been achieved due to the different nature of the characterization methods.
A fast and unbiased correlative imaging analysis method for heterogeneous spatial data sets has been developed by researchers. It enables quantitative correlative multi-technique functional material studies. The technique enables 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 that were not visible using traditional manual analysis and the origin of which can only be explained using the quantitative correlation between the two data sets.
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