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.