Modeling S-parameters of Interconnects using Periodic Gaussian Process Kernels

In this paper, we present a novel technique to model wide-band scattering parameter (S-parameter) curves of high-speed digital interconnects. The proposed technique utilizes a new kernel function with periodic components for Gaussian process (GP) models. After proper training, the GP models are able to predict the S-parameter values at arbitrary frequency points inside the trained interval. The performance of the proposed technique is reviewed by means of correlation with standard Gaussian Processes with squared exponential kernel and Matern kernel. Results for the proposed technique show an increased prediction accuracy when applied to interconnects.

Subjects

Gaussian processes (GP)

Interconnects

kernels

machine learning (ML)

S-parameters

MLE@TUHH

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Modeling S-parameters of Interconnects using Periodic Gaussian Process Kernels