Data-driven prediction of temperature-dependent dielectric and conductive parameters based on differential stripline characteristics

Start Page
1
End Page
4
Citation
29th IEEE Workshop on Signal and Power Integrity, SPI 2025
Contribution to Conference
29th IEEE Workshop on Signal and Power Integrity, SPI 2025  
Publisher DOI
10.1109/SPI64682.2025.11014374
Scopus ID
2-s2.0-105007753004
Publisher
IEEE
ISBN
979-8-3315-2061-8
979-8-3315-2062-5
The design of automotive high-speed interconnects requires the use of broadband material models that consider a wide range of environmental conditions, e.g. temperature and humidity. However, even for nominal conditions these models are nontrivial to generate. This paper extends our previous work on data-driven predictors used for the estimation of dielectric and conductive parameters of high-speed material models. Parametrized frequency-domain simulations of a differential stripline at various temperatures yield a dataset of modal propagation constants, and characteristic impedances up to 25 GHz. These inputs are used to train a 1D Convolutional Neural Network to estimate the temperature-dependent conductive and dielectric model parameters for the cannoball Huray and wideband Debye models, respectively. The trained model shows good accuracy after training/testing. Additional simulations outside the training/test dataset are used for further model validation with good agreement between predictions and expected values.
Subjects
automotive | CNN | data-driven | high-speed | interconnect | temperature
DDC Class
600: Technology