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Atomic-scale defect detection is shown in scanning tunneling microscopyimages of single crystal WSe2 using an ensemble of U-Net-like convolutionalneural networks. Standard deep learning test metrics indicated good detectionperformance with an average F1 score of 0.66 and demonstrated ensemblegeneralization to C-AFM images of WSe2 and STM images of MoSe2. Defectcoordinates were automatically extracted from defect detections maps showingthat STM image analysis enhanced by machine learning can be used todramatically increase sample characterization throughput.

Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning