MODVORTEx: computer vision-driven automation for magnetic domain wall velocity analysis

Abstract This paper presents Magneto Optic Domain Velocity Observation and Real-Time Extraction (MODVORTEx), a comprehensive software solution for automating domain wall (DW) velocity measurements in magnetic materials using magneto-optic Kerr effect microscopy. Building upon our previous work on bubble domain structures, we introduce a versatile graphical user interface (GUI) that accommodates arbitrary domain shapes and employs advanced computer vision techniques. The software provides different methods for DW detection and velocity calculation, catering to various domain structures of arbitrary shape. Our approach significantly reduces the time and effort required for data extraction, transforming a process that previously took days of manual work into a task completable within minutes. We provide the details of the algorithmic implementation which is organized into pre-processing, DW detection, displacement measurement, and velocity extraction. This tool is applicable to a wide range of scenarios, including bubble domain dynamics, Dzyaloshinskii–Moriya interaction studies in perpendicular magnetic anisotropy systems, current-driven DW motion in patterned strips etc. By providing both GUI and Application Programing Interface, our software offers flexibility for integration into existing measurement systems and adaptability for specific research needs. This automation promises to accelerate research in spintronics and magnetic materials, enabling more comprehensive and accurate studies of DW dynamics.

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Publication Details

Journal
Measurement Science and Technology
Published
2024-10-28
DOI
https://doi.org/10.1088/1361-6501/ad8beb
Citations
2
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
0.50
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article

MODVORTEx: computer vision-driven automation for magnetic domain wall velocity analysis

Rakhul Raj, Varimalla Raghavendra Reddy
2 citations
Measurement Science and Technology
Advanced Neural Network Applications
0.50
article

MODVORTEx: computer vision-driven automation for magnetic domain wall velocity analysis

Rakhul Raj, Varimalla Raghavendra Reddy
article en
2 citations

Abstract

Abstract This paper presents Magneto Optic Domain Velocity Observation and Real-Time Extraction (MODVORTEx), a comprehensive software solution for automating domain wall (DW) velocity measurements in magnetic materials using magneto-optic Kerr effect microscopy. Building upon our previous work on bubble domain structures, we introduce a versatile graphical user interface (GUI) that accommodates arbitrary domain shapes and employs advanced computer vision techniques. The software provides different methods for DW detection and velocity calculation, catering to various domain structures of arbitrary shape. Our approach significantly reduces the time and effort required for data extraction, transforming a process that previously took days of manual work into a task completable within minutes. We provide the details of the algorithmic implementation which is organized into pre-processing, DW detection, displacement measurement, and velocity extraction. This tool is applicable to a wide range of scenarios, including bubble domain dynamics, Dzyaloshinskii–Moriya interaction studies in perpendicular magnetic anisotropy systems, current-driven DW motion in patterned strips etc. By providing both GUI and Application Programing Interface, our software offers flexibility for integration into existing measurement systems and adaptability for specific research needs. This automation promises to accelerate research in spintronics and magnetic materials, enabling more comprehensive and accurate studies of DW dynamics.

Measurement Science and TechnologyVol. 36(1)
UGC DAE Consortium for Scientific Research (IN)
Openalex Percentile: Top 34%
Advanced Neural Network Applications
0.50
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MODVORTEx: computer vision-driven automation for magnetic domain wall velocity analysis — Rakhul Raj, Varimalla Raghavendra Reddy · Measurement Science and Technology (2024) | TGRS Research Map | TGRS