An Equivalent Dipole Model Hybrid with Artificial Neural Network for Electromagnetic Interference Prediction
Abstract
A new equivalent dipole model hybrid with artificial neural network (ANN) is proposed in this paper for electromagnetic interference (EMI) estimation. Equivalent dipole method, based on the free-space Green's function, is usually used to model unknown EMI sources on printed circuit boards. For high-speed and dense circuits, there may be multi-reflection and/or diffraction between the EMI source and its nearby components. Traditional dipole model usually omits such effects and leads to an inaccurate result in some cases. In our proposed method, the Green's function of dipole is taken as input and the radiated EMI field is taken as the output of ANN. We use the powerful mapping ability of ANN to modify the matrix-vector multiplication between free-space Green's function and dipole moments in the traditional dipole model, so that a new mapping between equivalent dipoles and their radiated fields is established. The near field of the EMI source is obtained by planar scanning, and is used for ANN training. After training, the ANN is used to predict the EMI field at the region of interest. Both numerical example and measurement example are given to show the effectiveness of the proposed ANN method. This paper provides a novel source reconstruction solution for the EMI problems.
Recommended Citation
Y. Shu et al., "An Equivalent Dipole Model Hybrid with Artificial Neural Network for Electromagnetic Interference Prediction," IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 5, pp. 1790 - 1797, Institute of Electrical and Electronics Engineers (IEEE), May 2019.
The definitive version is available at https://doi.org/10.1109/TMTT.2019.2905238
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Electromagnetic pulse; Electromagnetic wave interference; Image segmentation; Mapping; Neural networks; Numerical methods; Printed circuit boards; Vector spaces; Dipole methods; Dipole sources; Matrix vector multiplication; Multi-reflection; Near-field scanning; Planar scanning; Region of interest; Source reconstruction; Signal interference; Artificial neural network (ANN); Electromagnetic interference (EMI); Equivalent dipole source
International Standard Serial Number (ISSN)
0018-9480; 1557-9670
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 May 2019
Comments
This work was supported in part by the National Science Foundation of China under Grant 61871467, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LGG18F010002, in part by the Scientific Research Fund of Sichuan Provincial of China under Grant 18SYXHZ0056, and in part by the Key Lab of High Speed Circuit Design and EMC of Ministry of Education China.