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Progressive Expansion Sampling of Quasi-Static Magnetic Fields in Unconfined Regions

  • We present a fast and accurate measurement technique for quasi-static magnetic fields by employing a progressive sampling method in an unconfined input space. The proposed machine learning algorithm is tested against uniform sampling on printed circuit board test structures and a buck converter. We prove allocation of multiple, separated regions with predefined lateral field limits at MHz frequencies. The feasibility of equivalent magnetic dipole source modeling based on a small number of samples is demonstrated. Compared to uniform testing, progressive expansion sampling identifies contours of given field limits in less than 3% of the reference measurement time.
Metadaten
Author:Norbert SeligerORCiD, Georg Faltlhauser
DOI:https://doi.org/10.1109/TCPMT.2023.3283285
Parent Title (English):IEEE Transactions on Components, Packaging and Manufacturing Technology
Document Type:Article (peer reviewed)
Language:English
Publication Year:2023
Tag:Gaussian Process Classifier; Machine Learning; MoM modeling; Near-Field Scanning; Progressive Expansion Sampling
Page Number:9
Peer reviewed:Ja
faculties / departments:Fakultät für Ingenieurwissenschaften
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik / 600 Technik, Technologie