Machine Learning for Automating the Design of Millimeter-Wave Baluns

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 a...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2021-06, Vol.68 (6), p.2329-2340
Main Authors: Nguyen, Huy Thong, Peterson, Andrew F.
Format: Article
Language:English
Subjects:
Analytical models
Automation
Baluns
Computational modeling
Impedance
impedance transforming baluns
Integrated circuit modeling
Machine learning
Mathematical model
Mathematical models
millimeter wave
Millimeter waves
mm-wave
Optimization
Parameters
Physical properties
Power amplifiers
Transistors
Trial and error methods
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Summary: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.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2021.3068303