An approach for automated scale invariant STM-scan matching using SIFT

In this paper, we present our research on automated matching of Scanning Tunneling Microscope (STM) images acquired at different scales and resolutions. Scale Invariant Feature Transform (SIFT [1]) is a method of selecting, describing, and matching key points of images. We apply SIFT to images generated with an STM to detect corresponding points and calculate exact transformation matrices. The matching procedure is modified in order to improve the results in this context. The long term goal of our research is to perform automated atom manipulation with an STM. For this purpose, the abilities to build a map of the surface, to recognize the position of the STM tip, and to handle spatial uncertainties are important for sophisticated control algorithms. SIFT has several possible applications in this context, for instance drift measurements and hierarchical maps for tip navigation.