| Title | Generic Modeling of Differential Striplines Using Machine Learning Based Regression Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Penugonda, S., Yong, S., Gao, A., Cai, K., Sen, B., Fan, J. |
| Conference Name | 2020 IEEE International Symposium on Electromagnetic Compatibility Signal/Power Integrity (EMCSI) |
| Date Published | jul |
| Keywords | ANN, compositionality, data points, design of experiments, dielectric losses, differential stripline generic modeling, DoE, electrical engineering computing, expandability, frequency-dependent dielectric loss, generic model, Hafnium, learning (artificial intelligence), linear regression, linear regression model, machine learning, machine learning based regression analysis, pubcrawl, recursive approach, regression analysis, Resiliency, strip lines, tabular W-element model, TensorFlow |
| Abstract | In this paper, a generic model for a differential stripline 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 stripline 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 striplines in the frequency range of 10 MHz to 20 GHz. |
| DOI | 10.1109/EMCSI38923.2020.9191490 |
| Citation Key | penugonda_generic_2020 |
Generic Modeling of Differential Striplines Using Machine Learning Based Regression Analysis