Scanning Tunnelling State Recognition With Multi-Class Neural Network Ensembles Scanning Tunnelling State Recognition With Multi-Class Neural Network Ensembles

Gordon, O.; D'hondt, P.; Knijff, L.; Freeney, S.E.; Junqueira, F.; Moriarty, P.; Swart, I.

Abstract

One of the largest obstacles facing scanning probe microscopy is the constant need to correct flaws in the scanning probe in situ. This is currently a manual, time-consuming process that would benefit greatly from automation. Here we introduce a convolutional neural network protocol that enables automated recognition of a variety of desirable and undesirable scanning tunnelling tip states on both metal and non-metal surfaces. By combining the best performing models into majority voting ensembles, we find that the desirable states of H:Si(100) can be distinguished with a mean precision of 0.89 and an average receiver-operator-characteristic curve area of 0.95. More generally, high and low-quality tips can be distinguished with a mean precision of 0.96 and near perfect area-under-curve of 0.98. With trivial modifications, we also successfully automatically identify undesirable, non-surface-specific states on surfaces of Au(111) and Cu(111). In these cases we find mean precisions of 0.95 and 0.75 and area-under-curves of 0.98 and 0.94, respectively. Provided that training data is available, these ensembles therefore enable fully autonomous scanning tunnelling state recognition for a wide range of typical scanning conditions.

Citation

Gordon, O., D'hondt, P., Knijff, L., Freeney, S., Junqueira, F., Moriarty, P., & Swart, I. (2019). Scanning Tunnelling State Recognition With Multi-Class Neural Network Ensembles Scanning Tunnelling State Recognition With Multi-Class Neural Network Ensembles. Review of Scientific Instruments, 90(10), Article 103704. https://doi.org/10.1063/1.5099590

• Journal Article Type: Article
• Acceptance Date: Sep 6, 2019
• Online Publication Date: Oct 11, 2019
• Publication Date: Oct 11, 2019
• Deposit Date: Sep 16, 2019
• Publicly Available Date: Sep 16, 2019
• Journal: Review of Scientific Instruments
• Print ISSN: 0034-6748
• Electronic ISSN: 1089-7623
• Publisher: American Institute of Physics
• Peer Reviewed: Peer Reviewed
• Volume: 90
• Issue: 10
• Article Number: 103704
• DOI: https://doi.org/10.1063/1.5099590
• Keywords: Instrumentation
• Public URL: https://nottingham-repository.worktribe.com/output/2621646
• Publisher URL: https://aip.scitation.org/doi/10.1063/1.5099590
• Additional Information: Received: 2019-04-11; Accepted: 2019-09-06; Published: 2019-10-11
• Contract Date: Sep 16, 2019