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Machine learning for complex EMI prediction, optimization and localization

The electromagnetic interference (EMI) problem of extra-high speed electronic devices and systems is becoming more complex with an increase of operating frequency. The conventional analysis and design methods could not cope with the current EMI problems. Advanced analysis and design methods are desired. Deep neural network (DNN) and Bayesian optimization algorithm (BOA) based on machine learning are utilized in prediction of EMI radiation, optimization of design parameters and localization of EMI sources. The feasibility of DNN and BOA is investigated and validated. The steps of using DNN and BOA are proposed in the paper.