Inverse Design of Transmission Lines with Deep Learning

Kallol Roy, Majid Ahadi Dolatsara, Hakki M. Torun, Riccardo Trinchero, Madhavan Swaminathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Design of microwave structures and tuning parameters have mostly relied on the domain expertise of circuit designers by doing many simulations, which can be prohibitively time consuming. An inverse problem approach suggests going in the opposite direction to determine design parameters from characteristics of the desired output. In this work, we propose a novel machine learning architecture that circumvents usual design method for given quality of eye characteristics by means of a Lifelong Learning Architecture. Our proposed machine learning architecture is a large-scale coupled training system in which multiple predictions and classifications are done jointly for inverse mapping of transmission line geometry from eye characteristics. Our model is trained in a guided manner by using intra-tasks results, common Knowledge Base (KB), and coupling constraints. Our method of inverse design is general and can be applied to other applications.

Original languageEnglish (US)
Title of host publication2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728145853
DOIs
StatePublished - Oct 2019
Event28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019 - Montreal, Canada
Duration: Oct 6 2019Oct 9 2019

Publication series

Name2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019

Conference

Conference28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019
Country/TerritoryCanada
CityMontreal
Period10/6/1910/9/19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials

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