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Novel method for predicting PCB configurations for near-field and far-field radiated EMI using a neural network
Author(s)
Aunchaleevarapan, Kraison
Paithoonwatanakij, Kitti
Khan-Ngern, Werachet
Nitta, Shuichi
Date Issued
January 1, 2003
Type
Article
Abstract
The Neural Network (NN) is applied to recognize basic PCB configurations using its magnetic near-field spectra and radiated far-field emission. The learning process is accomplished by using the computed spectra of the radiated field from PCBs having different configurations. The anomaly is detected through the monitoring of the spectra's amplitude frequency by injecting a voltage pulse at the PCB configuration. The trained NN is then applied to the identification of PCB layouts from radiated emission measurements. The trained NN can identify all of those PCB configurations from the magnetic near-field spectra and the radiated far-field EMI. Moreover, the calculated results of the NN are compared with the actual far-field measurements and other models for evaluation. Finally, the NN used for predicting far-field emission from their magnetic near-field measurement is proposed. Experiments show that the NN can predict the far-field spectra from the magnetic near-field spectra.
Citation
IEICE Transactions on Communications, E86-B(4), 1364-1375, 2003