Transfer Learning Framework for 3-D Electromagnetic Applications

In the realm of machine-learning (ML)-based electronic design automation (EDA), several factors contribute to inefficiency, posing various challenges. Initially, the lack of flexibility in input structures hinders the sharing of information across different circuit topologies. In addition, substantial costs are incurred in terms of simulation run times during the data generation process due to the necessity of creating a large training dataset for each circuit topology. To this effect, in this article, we address the dual problem of how to: 1) develop a general unified surrogate model that can handle a variety of circuit topologies and 2) employ previously trained models and adapt them to new models. We provide a formulation for transforming 3-D electromagnetic (EM) circuits into versatile circuit graphs, for a variety of topologies, imbued with structural information. The absence of such frameworks represents a gap in ML-based EDA, which we fill by providing a set of building blocks to achieve significant improvements in modeling tasks. Finally, we present a versatile forward modeling framework that allows one to quickly obtain the output response given a set of design parameters. We achieve the overarching goal of reducing the resources needed to create an ML model library for signal integrity (SI) applications in microelectronics packaging.