Determination of tip transfer function for quantitative MFM using frequency domain filtering and least squares method

. 2019 Mar 07 ; 9 (1) : 3880. [epub] 20190307

Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid30846777

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)

Odkazy

PubMed 30846777
PubMed Central PMC6405750
DOI 10.1038/s41598-019-40477-x
PII: 10.1038/s41598-019-40477-x
Knihovny.cz E-zdroje

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.

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