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This study develops a deep learning procedure able to identify a planar coil geometry, given the desired magnetic field map. This approach demonstrates its capability to discover suitable coil designs that produce desired field characteristics with high accuracy and efficiency. The generated coils show strong agreement with target magnetic fields, enabling manufacturers to achieve simpler structures and improved performance. This method is suitable for inductive proximity sensors, wireless power transfer systems, and electromagnetic compatibility applications, offering a powerful and flexible tool for advanced planar coil design.

Details

1009240
Subject
Simulation;
Physics;
Deep learning;
Magnetic fields;
Printed circuit boards;
Optimization;
Neural networks;
Heating;
Data processing;
Numerical analysis;
Design;
Geometry;
Product development;
Efficiency
Business indexing term
Identifier / keyword
magnetic field; planar coils; deep learning; CNN-based coil geometry models
Title
Deep Neural Network-Based Design of Planar Coils for Proximity Sensing Applications
Author
Lalla Abderraouf 1   VIAFID ORCID Logo  ; Di Barba Paolo 1   VIAFID ORCID Logo  ; Hausman Sławomir 2   VIAFID ORCID Logo  ; Mognaschi Maria Evelina 1   VIAFID ORCID Logo 

1  Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy; [email protected] (P.D.B.); [email protected] (M.E.M.) 
2  Institute of Electronics, Lodz University of Technology, Al. Politechniki 8, 93-590 Lodz, Poland; [email protected] 
Publication title
Sensors; Basel
Volume
25
Issue
14
First page
4429
Number of pages
25
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
Biology--Biochemistry, Chemistry--Analytical Chemistry
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-07-16
Milestone dates
2025-06-12 (Received); 2025-07-15 (Accepted)
Publication history
 
 
   First posting date
16 Jul 2025
DOI
https://doi.org/10.3390/s25144429
ProQuest document ID
3233261828
Document URL
https://www.proquest.com/scholarly-journals/deep-neural-network-based-design-planar-coils/docview/3233261828/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-07-25
Database
ProQuest One Academic