An automatic method for atom identification in scanning tunnelling microscopy images of Fe-chalcogenide superconductors.
We describe a computational approach for the automatic recognition and classification of atomic species in scanning tunnelling microscopy images. The approach is based on a pipeline of image processing methods in which the classification step is performed by means of a Fuzzy Clustering algorithm. As a representative example, we use the computational tool to characterize the nanoscale phase separation in thin films of the Fe-chalcogenide superconductor FeSex Te1-x , starting from synthetic data sets and experimental topographies. We quantify the stoichiometry fluctuations on length scales from tens to a few nanometres.
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.
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Perasso
IRCCS San Martino - IST, Genova, Italy.
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Toraci
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.
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Massone
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.|Dipartimento di Matematica, Università di Genova, Genova, Italy.
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Piana
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.
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Gerbi
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.
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Buzio
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.
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Kawale
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.
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Bellingeri
CNR-SPIN Institute for Superconductors, Innovative Materials and Devices, Genova, Italy.
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Ferdeghini
Atoms; fuzzy clustering; image analysis; iron-chalcogenide; pattern recognition; scanning tunnelling microscopy; superconductors; thin films