  Atomic scale characterization and manipulation with scanning probe microscopy
rely upon the use of an atomically sharp probe. Here we present automated
methods based on machine learning to automatically detect and recondition the
quality of the probe of a scanning tunneling microscope. As a model system, we
employ these techniques on the technologically relevant hydrogen-terminated
silicon surface, training the network to recognize abnormalities in the
appearance of surface dangling bonds. Of the machine learning methods tested, a
convolutional neural network yielded the greatest accuracy, achieving a
positive identification of degraded tips in 97% of the test cases. By using
multiple points of comparison and majority voting, the accuracy of the method
is improved beyond 99%. The methods described here can easily be generalized to
other material systems and nanoscale imaging techniques.
