Automated Scanning Probe Tip State Classification without Machine Learning

The manual identification and in situ correction of the state of the scanning probe tip is one of the most time-consuming and tedious processes in atomic-resolution scanning probe microscopy. This is due to the random nature of the probe tip on the atomic level, and the requirement for a human operator to compare the probe quality via manual inspection of the topographical images after any change in the probe. Previous attempts to automate the classification of the scanning probe state have focused on the use of machine learning techniques, but the training of these models relies on large, labeled data sets for each surface being studied. These data sets are extremely time-consuming to create and are not always available, especially when considering a new substrate or adsorbate system. In this paper, we show that the problem of tip classification from a topographical image can be solved by using only a single image of the surface along with a small amount of prior knowledge of the appearance of the system in question with a method utilizing template matching (TM). We find that by using these TM methods, comparable accuracy and precision can be achieved to values obtained with the use of machine learning. We demonstrate the efficacy of this technique by training a machine learning-based classifier and comparing the classifications with the TM classifier for two prototypical silicon-based surfaces. We also apply the TM classifier to a number of other systems where supervised machine learning-based training was not possible due to the nature of the training data sets. Finally, the applicability of the TM method to surfaces used in the literature, which have been classified using machine learning-based methods, is considered.