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Modeling electrically long interconnects using physics-informed delayed gaussian processes
Publikationstyp
Journal Article
Publikationsdatum
2023-12-01
Sprache
English
Author
Garbuglia, Federico
Dhaene, Tom
Volume
65
Issue
6
Start Page
1715
End Page
1723
Citation
IEEE Transactions on Electromagnetic Compatibility 65 (6): 1715-1723 (2023-12)
Publisher DOI
Scopus ID
Publisher
IEEE
This work presents a machine learning technique to model wide-band scattering parameters (S-parameters) of interconnects in the frequency domain using a new Gaussian processes (GP) model. Standard GPs with a general-purpose kernel typically assume high smoothness and therefore are not suitable to model S-parameters that are highly dynamic and oscillating due to propagation delays. The new delayed Gaussian process (<inline-formula><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula>GP) model employs a physics-informed kernel consisting of periodic components, whose fundamental frequencies are interpreted as tunable propagation delays. Then, the model hyperparameters are tuned using a combination of maximum marginal likelihood estimation (MMLE) and delay estimation using Gabor transform. The delay estimation allows one to automatically identify the optimal fundamental frequencies for the kernel, thus increasing the numerical stability of the hyperparameters tuning process. The resulting delayed Gaussian process model accurately predicts the S-parameter values at desired frequency points in the training interval. Two application examples demonstrate the increased accuracy of the new technique, compared to standard Gaussian processes, vector fitting (VF), and delayed vector fitting (DVF) rational models.
Schlagworte
Computational modeling
Data models
Delay estimation
Estimation
Gabor transform
Gaussian processes (GP)
interconnects
Kernel
kernels
machine learning (ML)
Propagation delay
Scattering parameters
S-parameters
Transforms
MLE@TUHH
DDC Class
004: Computer Sciences