AI-Enhanced Modal Decomposition Method for Fast and Efficient PCB Modeling and Signal Integrity

An AI-enhanced modal decomposition method is proposed in this paper for fast and efficient high-speed multilayered PCB modeling and integrity. Depending on modal patterns of PCB local structures, modal decomposition method divides a given high-speed PCB interconnections into independent modal cells,...

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Bibliographic Details
Published in2023 IEEE 7th International Symposium on Electromagnetic Compatibility (ISEMC) pp. 1 - 4
Main Authors Gao, Zhaodong, Su, Ming, Guo, Xingyue, Chen, Xilian, Yan, Chuming, Liu, Yuanan
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.10.2023
Subjects
Artificial Intelligence
Computational modeling
Computer architecture
Data models
Electric potential
Electromagnetic compatibility
Machine learning
Measurement uncertainty
Modal Decomposition
PCB Modeling
Predictive models
Signal Integrity
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DOI10.1109/ISEMC58300.2023.10370118

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Summary:An AI-enhanced modal decomposition method is proposed in this paper for fast and efficient high-speed multilayered PCB modeling and integrity. Depending on modal patterns of PCB local structures, modal decomposition method divides a given high-speed PCB interconnections into independent modal cells, and take advantages of different evaluation methods (analytical, numerical, AI-based) to analyze each modal cell. With the assistance of AI technology, compound methods find a way to compute complex PCB structures effectively in this modal decomposition method. We briefly introduced the architecture and workflow of the proposed method, and then gave a practical application example to show the validity. Data show frequency error of 2.3/1.4% and amplitude error of 0.3/0.6dB for 0-28/28-40GHz between measurement and model prediction, showing great potentials of the AI-enhanced modal decomposition method for PCB modeling and signal integrity.
DOI:10.1109/ISEMC58300.2023.10370118