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A Novel Hybrid Algorithm for Source Reconstruction Method in Near-field Prediction
  • +1
  • Chenxi Li,
  • Jian Pang,
  • Qingzhi Wu,
  • Yuehang Xu
Chenxi Li
University of Electronic Science and Technology of China School of Electronic Science and Engineering
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Jian Pang
Shanghai Jiao Tong University
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Qingzhi Wu
University of Electronic Science and Technology of China School of Electronic Science and Engineering

Corresponding Author:qzwu@uestc.edu.cn

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Yuehang Xu
University of Electronic Science and Technology of China School of Electronic Science and Engineering
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Abstract

Advanced packaging in electronic systems presents new challenges for electromagnetic interference issues. The source reconstruction method (SRM) based on near-field scanning provides a solution for locating electromagnetic interference sources and reconstructing the electromagnetic field inside the package. The traditional SRM based on least squares methods relies on phase information, leading to expensive measurement facilities and complex testing processes. As a result, phaseless SRMs with lower testing requirement have become a research hotspot. However, these methods require solving a nonlinear equation, which lacks an explicit solution and poses difficulties in extracting the equivalent radiation source. To address this issue, a new phaseless SRM that achieves high precision and efficiency is proposed. The method combines the advantages of differential evolution (DE) algorithm with the covariance matrix adaptation evolution strategy (CMA-ES) algorithm, offering fast convergence speed and high accuracy. Compared to conventional DE algorithm, the proposed hybrid method reduces the error of the reconstructed field on an average of 9% and improves the accuracy of the predicted field from 82% to 85% while accelerating convergence.
18 Jan 2025Submitted to International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
19 Jan 2025Submission Checks Completed
19 Jan 2025Assigned to Editor
20 Jan 2025Review(s) Completed, Editorial Evaluation Pending
31 Jan 2025Reviewer(s) Assigned