  Atomic-scale defect detection is shown in scanning tunneling microscopy
images of single crystal WSe2 using an ensemble of U-Net-like convolutional
neural networks. Standard deep learning test metrics indicated good detection
performance with an average F1 score of 0.66 and demonstrated ensemble
generalization to C-AFM images of WSe2 and STM images of MoSe2. Defect
coordinates were automatically extracted from defect detections maps showing
that STM image analysis enhanced by machine learning can be used to
dramatically increase sample characterization throughput.
