Scanning Probe State Recognition With Multi-Class Neural Network Ensembles
This work addresses the need for automation in scanning probe microscopy to reduce manual effort, though it appears incremental as it applies existing neural network methods to a new domain-specific task.
The paper tackled the problem of automating the manual and time-consuming correction of scanning probe flaws in microscopy by introducing a convolutional neural network protocol for recognizing desirable and undesirable tip states on various surfaces, achieving mean precisions up to 0.96 and area-under-curve values up to 0.98.
One of the largest obstacles facing scanning probe microscopy is the constant need to correct flaws in the scanning probe in situ. This is currently a manual, time-consuming process that would benefit greatly from automation. Here we introduce a convolutional neural network protocol that enables automated recognition of a variety of desirable and undesirable scanning probe tip states on both metal and non-metal surfaces. By combining the best performing models into majority voting ensembles, we find that the desirable states of H:Si(100) can be distinguished with a mean precision of 0.89 and an average receiver-operator-characteristic curve area of 0.95. More generally, high and low-quality tips can be distinguished with a mean precision of 0.96 and near perfect area-under-curve of 0.98. With trivial modifications, we also successfully automatically identify undesirable, non-surface-specific states on surfaces of Au(111) and Cu(111). In these cases we find mean precisions of 0.95 and 0.75 and area-under-curves of 0.98 and 0.94, respectively.