DOI: 10.1063/1.4948601. G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics

G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics

10.1063/1.4948601

Crossref journal-article

AIP Publishing

Applied Physics Letters (317)

Abstract

In this work, we develop a full information capture approach for Magnetic Force Microscopy (MFM), referred to as generalized mode (G-Mode) MFM. G-Mode MFM acquires and stores the full data stream from the photodetector, captured at sampling rates approaching the intrinsic photodiode limit. The data can be subsequently compressed, denoised, and analyzed, without information loss. Here, G-Mode MFM is implemented and compared to the traditional heterodyne-based MFM on model systems, including domain structures in ferromagnetic Yttrium Iron Garnet and the electronically and magnetically inhomogeneous high entropy alloy, CoFeMnNiSn. We investigate the use of information theory to mine the G-Mode MFM data and demonstrate its usefulness for extracting information which may be hidden in traditional MFM modes, including signatures of nonlinearities and mode-coupling phenomena. Finally, we demonstrate detection and separation of magnetic and electrostatic tip-sample interactions from a single G-Mode image, by analyzing the entire frequency response of the cantilever. G-Mode MFM is immediately implementable on any atomic force microscopy platform and as such is expected to be a useful technique for probing spatiotemporal cantilever dynamics and mapping material properties, as well as their mutual interactions.

Bibliography

Collins, L., Belianinov, A., Proksch, R., Zuo, T., Zhang, Y., Liaw, P. K., Kalinin, S. V., & Jesse, S. (2016). G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics. Applied Physics Letters, 108(19).

Authors 8

Liam Collins (first)
Alex Belianinov (additional)
Roger Proksch (additional)
Tingting Zuo (additional)
Yong Zhang (additional)
Peter K. Liaw (additional)
Sergei V. Kalinin (additional)
Stephen Jesse (additional)

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Dates

Type	When
Created	9 years, 3 months ago (May 9, 2016, 1 p.m.)
Deposited	2 years, 2 months ago (June 17, 2023, 11:20 a.m.)
Indexed	3 weeks, 4 days ago (July 30, 2025, 7:07 a.m.)
Issued	9 years, 3 months ago (May 9, 2016)
Published	9 years, 3 months ago (May 9, 2016)
Published Online	9 years, 3 months ago (May 9, 2016)
Published Print	9 years, 3 months ago (May 9, 2016)

Funders 5

Department of energy, office of fossil energy, National Energy Technology Laboratory
Awards1
1. DE-FE-0008855
Department of energy, Office of Fossil Energy, National Energy Technology Laboratory
Awards1
1. DE-FE-0011194
DOD | U.S. Army research office poject
Awards1
1. W911NF-13-1-0438
DOE, Office of Fossil Energy, National Energy Technology Laboratory
Awards1
1. DE-FE-0024054
National Sceience Foundation
Awards1
1. CMMI-1100080

BibTeX

@article{Collins_2016, title={G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics}, volume={108}, ISSN={1077-3118}, url={http://dx.doi.org/10.1063/1.4948601}, DOI={10.1063/1.4948601}, number={19}, journal={Applied Physics Letters}, publisher={AIP Publishing}, author={Collins, Liam and Belianinov, Alex and Proksch, Roger and Zuo, Tingting and Zhang, Yong and Liaw, Peter K. and Kalinin, Sergei V. and Jesse, Stephen}, year={2016}, month=may }

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