Machine Learning for Automating the Design of Millimeter-Wave Baluns

Abstract

We propose a framework to analyze mm-wave baluns directly from physical parameters by adding a dimension of Machine Learning (ML) to existing electromagnetic (EM) methods. From a generalized physical model of mm-wave baluns, we train physical-electrical Machine Learning models that both accurately and quickly compute the electrical parameters of mm-wave baluns from physical parameters, reducing the need for full-wave simulations and advancing several aspects of mm-wave designs. One of the advancements is a fully automated design process that accurately generates full EM designs of mm-wave baluns when given an electrical specification and a metal option. The automated technique only takes several seconds to complete, compared to hours-weeks of the current trial-and-error methods, and notably the approach can optimize mm-wave baluns directly for the lowest metal loss. Another advancement is the theoretical interpretation of several high-level and abstract questions concerning mm-wave designs, in which we quantify the optimum transistor sizes for the last stage of a class-AB differential power amplifier on an on-chip process and derive the rule of thumb describing the inverse relationship between the optimum device sizes and mm-wave frequencies.