Machine Learning for Automating Millimeter-Wave Electromagnetic Designs

dc.contributor.advisor Peterson, Andrew F.
dc.contributor.author Nguyen, Huy Thong
dc.contributor.committeeMember Cressler, John
dc.contributor.committeeMember Klein, Benjamin
dc.contributor.committeeMember Durgin, Gregory David
dc.contributor.committeeMember Nguyen, Thanh
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2021-01-11T17:12:16Z
dc.date.available 2021-01-11T17:12:16Z
dc.date.created 2020-12
dc.date.issued 2020-12-01
dc.date.submitted December 2020
dc.date.updated 2021-01-11T17:12:16Z
dc.description.abstract This dissertation proposes a general class of solution and Machine Learning (ML) techniques to support the designs of several critical Electromagnetic (EM) structures at mm-wave frequency ranges, for which the main applications are to directly address the emerging power and efficiency challenges of the next generation of wireless communication. Starting from the coupled line theory, we theoretically propose a common solution for Impedance Transforming Baluns, Power Combiners, Out-Phasing circuits, and Doherty networks, which we refer to as the BCOD structure. The main contribution of this dissertation is to develop Machine Learning (ML) techniques that, within the computational time of seconds, can fully automate EM designs of the BCOD network that achieves the lowest metal loss, in which we demonstrated the proposed ML approaches on various on-chip metal stacks for a wide range of electrical specifications. Notably, optimizing for the lowest metal loss is a challenging problem, and to the best of our knowledge, we are not aware of any prior techniques that can systematically do so. Importantly, the application of our proposed ML approaches can go beyond the task of automating EM structures. Serving as a new tool for bigger optimization loops, the ML techniques can theoretically answer several challenging, abstract, and high-level questions for mm-wave designs, such as the calculation of the optimum transistor sizes, or the derivation of the rule of thumb between device sizes and mm-wave frequencies.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/64157
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Automation, Electromagnetics, Machine Learning, Millimeter-Wave
dc.title Machine Learning for Automating Millimeter-Wave Electromagnetic Designs
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Peterson, Andrew F.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 080ab541-0779-41ab-ae29-90761a9f6649
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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