Autonomous Scanning Probe Microscopy Investigations over WS$_2$ and Au{111}

7 Oct 2021  ·  John C. Thomas, Antonio Rossi, Darian Smalley, Luca Francaviglia, Zhuohang Yu, Tianyi Zhang, Shalini Kumari, Joshua A. Robinson, Mauricio Terrones, Masahiro Ishigami, Eli Rotenberg, Edward Barnard, Archana Raja, Ed Wong, D. Frank Ogletree, Marcus Noack, Alexander Weber-Bargioni ·

Point defect identification in two-dimensional materials enables an understanding of the local environment within a given system, where scanning probe microscopy that takes advantage of hyperspectral tunneling bias spectroscopy acquisition can both map and identify the atomic and electronic landscape. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for defect identification... Monolayer semiconductor is explored on sulfur vacancies within tungsten disulfide (WS$_2$), to provide the first two-dimensional hyperspectral insight into available sulfur-substitution sites within a transition metal dichalcogenide, which is combined with spectral confirmation on the Au{111} herringbone reconstruction for both tip state verification and local fingerprinting, where face-centered cubic and hexagonal close packed regions are detected by machine learning methods. Acquired data enable image segmentation across the above mentioned defect modes, and a workflow is provided for both machine-driven decision making during experimentation and the capability for user customization. read more

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Materials Science Applied Physics