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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.

Authors

O. Gordon

P. D'hondt

L. Knijff

S.E. Freeney

F. Junqueira

P. Moriarty

I. Swart



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.

Journal Article Type Article
Publication Date Oct 11, 2019
Journal Review of Scientific Instruments
Print ISSN 0034-6748
Electronic ISSN 1089-7623
Publisher AIP Publishing
Peer Reviewed Peer Reviewed
Volume 90
Issue 10
Article Number 103704
APA6 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), https://doi.org/10.1063/1.5099590
DOI https://doi.org/10.1063/1.5099590
Keywords Instrumentation
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

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