Surrogate Modeling with Complex-valued Neural Nets and its Application to Design of sub-THz Patch Antenna-in-Package

Oluwaseyi Akinwande, Osama Waqar Bhatti, Kai Qi Huang, Xingchen Li, Madhavan Swaminathan

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

2 Scopus citations

Abstract

In this paper, we propose a surrogate model for both forward and inverse modeling with complex-valued neural networks. The complex domain offers the benefits of higher functionality and better representation. To that end, we propose a deep complex dense network (DNet) by introducing complex dense blocks built with fully-connected layers that support complex operations. We further propose an inverse optimization objective that minimizes the modeling error while optimizing the design space parameters that achieve the target specifications. We apply our proposed approach for the design of a sub-THz patch antenna-in-package operating at 140 GHz frequency band.

Original languageEnglish (US)
Title of host publication2023 IEEE/MTT-S International Microwave Symposium, IMS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages423-426
Number of pages4
ISBN (Electronic)9798350347647
DOIs
StatePublished - 2023
Event2023 IEEE/MTT-S International Microwave Symposium, IMS 2023 - San Diego, United States
Duration: Jun 11 2023Jun 16 2023

Publication series

NameIEEE MTT-S International Microwave Symposium Digest
Volume2023-June
ISSN (Print)0149-645X

Conference

Conference2023 IEEE/MTT-S International Microwave Symposium, IMS 2023
Country/TerritoryUnited States
CitySan Diego
Period6/11/236/16/23

All Science Journal Classification (ASJC) codes

  • Radiation
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

Cite this