Hernandez-Bonilla, Jose EnriqueJose EnriqueHernandez-BonillaWendt, TorbenTorbenWendtReuschel, TorstenTorstenReuschelYang, ChengChengYangSchuster, ChristianChristianSchuster2025-07-042025-07-042025-0529th IEEE Workshop on Signal and Power Integrity, SPI 2025979-8-3315-2061-8979-8-3315-2062-5https://hdl.handle.net/11420/56092The 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.enautomotive | CNN | data-driven | high-speed | interconnect | temperatureTechnology::600: TechnologyConference Paper10.1109/SPI64682.2025.11014374Conference Paper