Inverse Design of Embedded Inductor with Hierarchical Invertible Neural Transport Net

Oluwaseyi Akinwande, Osama Waqar Bhatti, Madhavan Swaminathan

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

1 Scopus citations

Abstract

Heterogeneous integration of voltage regulators in power delivery networks is a growing trend that employs em-bedded inductor as a key component in significantly improving the power distribution. In this work, we propose a neural network framework called the hierarchical invertible neural transport for the inverse design of an embedded inductor. With this invertible method, we obtain the probability distributions of the parameters of the embedded inductor design space that most likely satisfy the desired specifications. We also learn the impedance response for free in the forward design. In the forward design, our results show a 2.14% normalized mean square error when compared with the output response of a full wave EM simulator.

Original languageEnglish (US)
Title of host publicationEPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665450751
DOIs
StatePublished - 2022
Event31st IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2022 - San Jose, United States
Duration: Oct 9 2022Oct 12 2022

Publication series

NameEPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference31st IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2022
Country/TerritoryUnited States
CitySan Jose
Period10/9/2210/12/22

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials
  • Instrumentation

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