Optimization of modal reflection parameters of PCB connector and common-mode choke using Gaussian process regression and segmentation

In this contribution the electrical properties of a high-speed signal transition from a connector onto a printed circuit board (PCB) followed by a common-mode choke (CMC) in a surface mount package in terms of its modal, i.e., differentialand common-mode, reflection parameters has been investigated. An implementation of segmentation, i.e. domain decomposition and subsequent composition of subdomains, in combination with Gaussian process regression (GPR) to optimize the reflections of the PCB connectors and CMC integration on the board is presented and analyzed. GPR is used with Bayesian optimization to find an optimal set of design parameters for a 3D electromagnetic model developed in a full-wave electromagnetic field solver up to 20 GHz. The results indicate that an engineeringinformed domain decomposition strategy can reduce the computational burden of GPR-based optimization by lowering the per-evaluation full-wave cost. This surrogate-based optimization approach reduces the number of required full-wave simulations, enabling more efficient use of computational resources.

Content

Subjects

Gaussian Process Regression

Bayesian Optimization

high-speed interconnects

PCB connectors

machine learning

common-mode choke packages

design optimization

DDC Class

510: Mathematics

Files

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publisher‘s Creditline

Author accepted manuscript (AAM) with CC-BY license and without embargo.

Publication version

acceptedVersion

Download

Name

AAM_EMC_EUROPE_2026_PATTERN.pdf

Type

Main Article

Size

1.37 MB

Format

Adobe PDF