학술논문
Machine Learning Approaches for Transformer Modeling
Document Type
Conference
Author
Source
2022 18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD) Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2022 18th International Conference on. :1-4 Jun, 2022
Subject
Language
Abstract
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.