Determination of tip transfer function for quantitative MFM using frequency domain filtering and least squares method
Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
15SIB06
EC | Horizon 2020 (Horizon 2020 - Research and Innovation Framework Programme)
15SIB06
EC | Horizon 2020 (Horizon 2020 - Research and Innovation Framework Programme)
15SIB06
EC | Horizon 2020 (Horizon 2020 - Research and Innovation Framework Programme)
15SIB06
EC | Horizon 2020 (Horizon 2020 - Research and Innovation Framework Programme)
15SIB06
EC | Horizon 2020 (Horizon 2020 - Research and Innovation Framework Programme)
PubMed
30846777
PubMed Central
PMC6405750
DOI
10.1038/s41598-019-40477-x
PII: 10.1038/s41598-019-40477-x
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
Magnetic force microscopy has unsurpassed capabilities in analysis of nanoscale and microscale magnetic samples and devices. Similar to other Scanning Probe Microscopy techniques, quantitative analysis remains a challenge. Despite large theoretical and practical progress in this area, present methods are seldom used due to their complexity and lack of systematic understanding of related uncertainties and recommended best practice. Use of the Tip Transfer Function (TTF) is a key concept in making Magnetic Force Microscopy measurements quantitative. We present a numerical study of several aspects of TTF reconstruction using multilayer samples with perpendicular magnetisation. We address the choice of numerical approach, impact of non-periodicity and windowing, suitable conventions for data normalisation and units, criteria for choice of regularisation parameter and experimental effects observed in real measurements. We present a simple regularisation parameter selection method based on TTF width and verify this approach via numerical experiments. Examples of TTF estimation are shown on both 2D and 3D experimental datasets. We give recommendations on best practices for robust TTF estimation, including the choice of windowing function, measurement strategy and dealing with experimental error sources. A method for synthetic MFM data generation, suitable for large scale numerical experiments is also presented.
CEITEC Brno University of Technology Brno 63800 Czech Republic
Czech Metrology Institute Brno 63800 Czech Republic
Department of Physical Electronics Faculty of Science Masaryk University Brno 61137 Czech Republic
IFW Dresden Dresden 01069 Germany
National Physical Laboratory Teddington TW11 0LW United Kingdom
Physics Department Royal Holloway University of London Egham TW20 0EX United Kingdom
Physikalisch Technische Bundesanstalt Braunschweig 38116 Germany
Plasma Technologies CEITEC Masaryk University Brno 62500 Czech Republic
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Cohen G, et al. Reconstruction of surface potential from Kelvin probe force microscopy images. Nanotechnology. 2013;24:295702. doi: 10.1088/0957-4484/24/29/295702. PubMed DOI
Lan F, Jiang M, Tao Q, Wei F, Li G. Reconstruction of Kelvin probe force microscopy image with experimentally calibrated point spread function. Rev. Sci. Instrum. 2017;88:033704. doi: 10.1063/1.4978282. PubMed DOI
Machleidt T, Sparrer E, Kapusi D, Franke K-H. Deconvolution of Kelvin probe force microscopy measurements - methodology and application. Meas. Sci. Technol. 2009;20:084017. doi: 10.1088/0957-0233/20/8/084017. DOI
Pieralli C. Statistical estimation of point spread function applied to scanning near-field optical microscopy. Opt. Commun. 1994;108:203–208. doi: 10.1016/0030-4018(94)90649-1. DOI
Hug HJ, et al. Quantitative magnetic force microscopy on perpendicularly magnetized samples. J. Appl. Phys. 1998;83:5609. doi: 10.1063/1.367412. DOI
Vock S, et al. Magnetic vortex observation in FeCo nanowires by quantitative magnetic force microscopy. Appl. Phys. Lett. 2014;105:172409. doi: 10.1063/1.4900998. DOI
Li H, Wei D, Piramanayagam SN. Micromagnetic study of effect of tip-coating microstructure on the resolution of magnetic force microscopy. Appl. Phys. A. 2013;110:217–225. doi: 10.1007/s00339-012-7117-x. DOI
Li H, Wei D, Piramanayagam SN. Optimization of perpendicular magnetic anisotropy tips for high resolution magnetic force microscopy by micromagnetic simulations. Appl. Phys. A. 2013;112:985–991. doi: 10.1007/s00339-012-7459-4. DOI
Li J, Chen N, Wei D, Futamoto M. Micromagnetic studies of ultrahigh resolution magnetic force microscope tip coated by soft magnetic materials. IEEE T. Magn. 2015;51:2001005.
van Schendel PJA, Hug HJ, Stiefel B, Martin S, Güntherodt HJ. A method for the calibration of magnetic force microscopy tips. J. Appl. Phys. 2000;88:435–445. doi: 10.1063/1.373678. DOI
Vock S, et al. Quantitative magnetic force microscopy study of the diameter evolution of bubble domains in a Co/Pd multilayer. IEEE Transactions on Magnetics. 2011;47:2352. doi: 10.1109/TMAG.2011.2155630. DOI
Puttock R, et al. V-shaped domain wall probes for calibrated magnetic force microscopy. IEEE Transactions on Magnetics. 2017;53:1–5. doi: 10.1109/TMAG.2017.2694324. DOI
Panchal V, et al. Calibration of multi-layered probes with low/high magnetic moments. Scientific Reports. 2017;7:7224. doi: 10.1038/s41598-017-07327-0. PubMed DOI PMC
Marioni MA, et al. Halbach effect at the nanoscale from chiral spin textures. Nano Lett. 2018;18:2263–2267. doi: 10.1021/acs.nanolett.7b04802. PubMed DOI
Candocia FM, Svedberg EB, Litvinov D, Khizroev S. Deconvolution processing for increasing the resolution of magnetic force microscopy measurements. Nanotechnology. 2004;15:S575–S584. doi: 10.1088/0957-4484/15/10/014. DOI
Bányász Á, Mátyus E, Keszei E. Deconvolution of ultrafast kinetic data with inverse filtering. Radiat. Phys. Chem. 2005;72:235–242. doi: 10.1016/j.radphyschem.2004.02.005. DOI
Bányász Á, Keszei E. Model-free deconvolution of femtosecond kinetic data. J. Phys. Chem. A. 2006;110:6192–6207. doi: 10.1021/jp057486w. PubMed DOI
Bishop, T. E. et al. Blind image deconvolution: problem formulation and existing approaches. In Campisi, P. & Egiazarian, K. (eds) Blind Image Deconvolution: Theory and Applications (CRC Press, London, 2017).
Högbom JA. Aperture synthesis with a non-regular distribution of interferometer baselines. Astron. Astrophys. Suppl. 1974;15:417–426.
Pratt WK. Digital Image Processing. 3rd edn. New York: John Wiley & Sons; 2001.
Gobbel GT, Fike JR. A deconvolution method for evaluating indicator-dilution curves. Phys. Med. Biol. 1994;39:1833–1854. doi: 10.1088/0031-9155/39/11/004. PubMed DOI
Dabóczi T, Kollár I. Multiparameter optimization of inverse filtering algorithms. IEEE Trans. Instrum. Meas. 1996;45:417–421. doi: 10.1109/19.492758. DOI
Parruck B, Riad SM. Study and performance evaluation of 2 iterative frequency-domain deconvolution techniques. IEEE Trans. Instrum. Meas. 1984;33:281–287. doi: 10.1109/TIM.1984.4315225. DOI
Golub GH, Meurant G. Matrices, Moments and Quadrature with Applications. New Jersey: Princeton University Press; 2010.
Hestenes MR, Stiefel E. Methods of conjugate gradients for solving linear systems. J. Res. Nat. Bur. Stand. 1952;49:409–436. doi: 10.6028/jres.049.044. DOI
Nečas D, Klapetek P. One-dimensional autocorrelation and power spectrum density functions of irregular regions. Ultramicroscopy. 2013;124:13–19. doi: 10.1016/j.ultramic.2012.08.002. PubMed DOI
Nečas D, Klapetek P. Gwyddion: an open-source software for SPM data analysis. Cent. Eur. J. Phys. 2012;10:181–188.
Harris FJ. On the use of windows for harmonic analysis with the discrete Fourier transform. P. IEEE. 1978;66:51–83. doi: 10.1109/PROC.1978.10837. DOI
Blackman RB, Tukey JW. The Measurement of Power Spectra, From the Point of View of Communications Engineering. New York: Dover; 1959.
Kaiser, J. F. Digital filters. In Kuo, F. F. & Kaiser, J. F. (eds) System Analysis by Digital Computer (Wiley, New York, 1966).
Welch PD. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE T. Acoust. Speech. 1967;15:70–73.
Tukey, J. W. An introduction to the calculations of numerical spectrum analysis. In Harris, B. (ed.) Spectral Analysis of Time Series, 25–46 (Wiley, New York, 1967).
Goey, Z. M. et al. SPM toolbox, https://qmfm.empa.ch/ (2013).
Hofmann B. Regularization for Applied Inverse and Ill-Posed Problems. Leipzig: Vieweg+Teubner Verlag; 1986.
Golub GH, Heath M, Wahba G. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics. 1979;21:215–223. doi: 10.1080/00401706.1979.10489751. DOI
Rice, J. A. Choice of smoothing parameter in deconvolution problems. In Marron, J. S. (ed.) Function Estimates, vol. 59 of Contemporary Mathematics, 137–151 (1986).
Desbat L, Girard D. The ‘minimum reconstruction-error’ choice of regularization parameters: some more efficient methods and their application to deconvolution problems. Siam. J. Sci. Comput. 1995;16:1387–1403. doi: 10.1137/0916080. DOI
Varah JM. Pitfalls in the numerical solution of linear ill-posed problems. SIAM J. Sci. and Stat. Comput. 1983;4:164–176. doi: 10.1137/0904012. DOI
Hall P, Titterington DM. Common structure of techniques for choosing smoothing parameters in regression problems. J. R. Stat. Soc. B. Met. 1987;49:184–198.
Engl HW, Grever W. Using the L-curve for determining optimal regularization parameters. Numerische Mathematik. 1994;69:25–31. doi: 10.1007/s002110050078. DOI
Vogel CR. Non-convergence of the L-curve regularization parameter selection method. Inverse Probl. 1996;12:535–547. doi: 10.1088/0266-5611/12/4/013. DOI
Frigo M, Johnson SG. The design and implementation of FFTW3. Proceedings of the IEEE. 2005;93:216–231. doi: 10.1109/JPROC.2004.840301. DOI
Shih FY. Image Processing and Mathematical Morphology: Fundamentals and Applications. Boca Raton: CRC Press; 2009.
Synthetic Data in Quantitative Scanning Probe Microscopy