Abstract
In this paper, a generic model for a differential strip line is created using machine learning (ML) based regression analysis. A recursive approach of creating various inputs is adapted instead of traditional design of experiments (DoE) approach. This leads to reduction of number of simulations as well as control the data points required for performing simulations. The generic model is developed using 48 simulations. It is comparable to the linear regression model, which is obtained using 1152 simulations. Additionally, a tabular W-element model of a differential strip line is used to take into consideration the frequency-dependent dielectric loss. In order to demonstrate the expandability of this approach, the methodology was applied to two differential pairs of strip lines in the frequency range of 10 MHz to 20 GHz.
Recommended Citation
S. Penugonda et al., "Generic Modeling of Differential Striplines using Machine Learning based Regression Analysis," 2020 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2020, pp. 226 - 230, article no. 9191490, Institute of Electrical and Electronics Engineers, Jul 2020.
The definitive version is available at https://doi.org/10.1109/EMCSI38923.2020.9191490
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
ANN; design of experiments; generic model; linear regression; machine learning; tensorflow
International Standard Book Number (ISBN)
978-172817430-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jul 2020
Comments
National Science Foundation, Grant 1916535