  As the development of atom scale devices transitions from novel,
proof-of-concept demonstrations to state-of-the-art commercial applications,
automated assembly of such devices must be implemented. Here we present an
automation method for the identification of defects prior to atomic fabrication
via hydrogen lithography using deep learning. We trained a convolutional neural
network to locate and differentiate between surface features of the
technologically relevant hydrogen-terminated silicon surface imaged using a
scanning tunneling microscope. Once the positions and types of surface features
are determined, the predefined atomic structures are patterned in a defect-free
area. By training the network to differentiate between common defects we are
able to avoid charged defects as well as edges of the patterning terraces.
Augmentation with previously developed autonomous tip shaping and patterning
modules allows for atomic scale lithography with minimal user intervention.
