Machine Learning Approaches for Transformer Modeling

In this paper, several machine learning modeling methodologies are applied to accurately and efficiently model transformers, which are still a bottleneck in millimeter-wave circuit design. In order to compare the models, a statistical validation is performed against electromagnetic simulations using hundreds of passive structures. The presented models using machine learning techniques have proven to be accurate, efficient, and useful for a wide range of frequencies from (around) DC up to the millimeter-wave range (around 100GHz). As an application example, the models are used as a performance evaluator in a synthesis procedure to optimize a transformer and a balun.