An automatic method for atom identification in Scanning Tunneling Microscopy images of Fe-chalcogenide superconductors

Description

We describe a computational approach for the automaticrecognition and classification of atomic species in scanningtunnelling microscopy images. The approach is based on apipeline of image processing methods in which the classifica-tion step is performed by means of a Fuzzy Clustering algo-rithm. As a representative example, we use the computationaltool to characterize the nanoscale phase separation in thinfilms of the Fe-chalcogenide superconductor FeSexTe1-x,start-ing from synthetic data sets and experimental topographies.We quantify the stoichiometry fluctuations on length scalesfrom tens to a few nanometres.

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