Embedding human heuristics in machine-learning-enabled probe microscopy

Gordon, Oliver M; Junqueira, Filipe L Q; Moriarty, Philip J

Abstract

Scanning probe microscopists generally do not rely on complete images to assess the quality of data acquired during a scan. Instead, assessments of the state of the tip apex, which not only determines the resolution in any scanning probe technique, but can also generate a wide array of frustrating artefacts, are carried out in real time on the basis of a few lines of an image (and, typically, their associated line profiles.) The very small number of machine learning approaches to probe microscopy published to date, however, involve classifications based on full images. Given that data acquisition is the most time-consuming task during routine tip conditioning, automated methods are thus currently extremely slow in comparison to the tried-and-trusted strategies and heuristics used routinely by probe microscopists. Here, we explore various strategies by which different STM image classes (arising from changes in the tip state) can be correctly identified from partial scans. By employing a secondary temporal network and a rolling window of a small group of individual scanlines, we find that tip assessment is possible with a small fraction of a complete image. We achieve this with little-to-no performance penalty—or, indeed, markedly improved performance in some cases—and introduce a protocol to detect the state of the tip apex in real time.

Citation

Gordon, O. M., Junqueira, F. L. Q., & Moriarty, P. J. (2020). Embedding human heuristics in machine-learning-enabled probe microscopy. Machine Learning, 1(1), Article 015001. https://doi.org/10.1088/2632-2153/ab42ec

• Journal Article Type: Article
• Acceptance Date: Sep 10, 2019
• Online Publication Date: Feb 4, 2020
• Publication Date: Mar 1, 2020
• Deposit Date: Feb 24, 2020
• Publicly Available Date: Feb 24, 2020
• Journal: Machine Learning: Science and Technology
• Print ISSN: 0885-6125
• Electronic ISSN: 1573-0565
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Issue: 1
• Article Number: 015001
• DOI: https://doi.org/10.1088/2632-2153/ab42ec
• Public URL: https://nottingham-repository.worktribe.com/output/3918817
• Publisher URL: https://iopscience.iop.org/article/10.1088/2632-2153/ab42ec
• Additional Information: Journal title: Machine Learning: Science and Technology; Article type: paper; Article title: Embedding human heuristics in machine-learning-enabled probe microscopy; Copyright information: © 2020 The Author(s). Published by IOP Publishing Ltd; License information: cc-by Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.; Date received: 2019-07-31; Date accepted: 2019-09-10; Online publication date: 2020-02-04