Generic Modeling of Differential Striplines Using Machine Learning Based Regression Analysis

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.

I. Introduction

As the appetite for fast data transmission rates keeps increasing, the engineers are faced with a task of designing and optimizing complex interconnects up to several tens of GHz. To reduce the simulation times, a ML based regression analysis methodology is used to create generic models (i.e. “black-box”) of differential striplines. Generic models enable engineers with little electromagnetic knowledge to develop PCB structures.