S-Parameters from a Neural Network

Hardware design for radio frequency devices often demands the use of full-wave electromagnetic (EM) field-solvers to extract scattering parameters (S-parameters) for systems that contain discontinuities throughout their printed circuit boards (PCBs) and substrate packages. Using EM field-solvers not only requires extensive EM knowledge during the setup phase, but also demands considerable computational resources. Previously, machine learning (ML) and neural network (NN) models have been used to solve EM fields; however, they tend to use design parameters as input rather than the structure’s geometry. Parametric analysis is beneficial when the solution space is well bounded, but it is more desirable to develop the capability of feeding generic and arbitrary geometry into a machine learning model for rapid S-parameter extraction. This work presents an algorithm for converting a sub-section of hardware geometry designed with an electronic design automation (EDA) tool into a dataset suitable for training and validating machine learning models including fully connected NNs and convolutional NNs (CNNs).

In our work, we demonstrate a novel Neural Network (NN) based methodology for generating S-Parameters based on using the meshed and discretized geometry as input to the NN. Our approach would enable a designer to rapidly optimize the performance of vias across an entire substrate. This performance is possible while also achieving a computational time improvement of approximately double that in other NN-based works.