Automated Scanning Probe Tip State Classification without Machine Learning.

ABSTRACT: 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

SUBMITTER: Barker DS 

PROVIDER: S-EPMC10811750 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature