Berlin 2024 – scientific programme
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O: Fachverband Oberflächenphysik
O 93: Scanning Probe Techniques: Method Development
O 93.11: Talk
Thursday, March 21, 2024, 17:30–17:45, MA 043
Image interpretation methods for high-resolution SPM — •Lauri Kurki1, Niko Oinonen1,2, and Adam S. Foster1,3 — 1Aalto University, Finland — 2Nanolayers Research Computing Ltd., UK — 3WPI-NanoLSI, Kanazawa University, Japan
Scanning tunnelling microscopy (STM) and atomic force microscopy (AFM) functionalized with a CO molecule on the probe apex capture sub-molecular level detail of the electronic and physical structures of a sample from different prespectives [1]. However, the produced images are often difficult to interpret. To accelerate the analysis, we propose automated machine learning image interpretation tools to extract sample properties directly from SPM images.
In recent years, there has been rapid development in image analysis methods using machine learning, with particular impact in medical imaging. These concepts have been proven effective also in SPM in general and in particular for extracting sample properties from AFM images [2,3,4]. We build upon these models and show that we can extract atomic positions directly from STM images. We also further explore how the accuracy of these predictions varies with the use of a simultaneous AFM signal. Finally, we establish the limits of the approach in an experimental context by predicting atomic structures from STM images of 2D ice structures.
[1] Cai et al. J. Am. Chem. Soc. 2022, 144, 44, 20227-20231 [2] Alldritt et al., Sci. Adv. 2020; 6 : eaay6913 [3] Carracedo-Cosme et al., Nanomaterials 2021, 11, 1658. [4] Oinonen et al., MRS Bulletin 2022, 47, 895-905
Keywords: Scanning tunnelling microscopy; Atomic force microscopy; Scanning probe microscopy; Machine learning