Applying Neural Network for Extending Cavity Perturbation: Extraction of Complex Permittivity in Non-Linear Region

Ahmad Khusro, Zubair Akhter, Atif Shamim, Abhishek K. Jha, Mohammad S. Hashmi

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

Abstract

To ensure precise modeling and simulation of microwave circuits and devices using CAD tools, it is essential to have a thorough knowledge of the dielectric properties of microwave substrates. However, in nonlinear regions, inaccurate characterization can occur as electromagnetic perturbation assumptions get invalid. The paper presents a novel neural network (NN) model, for accurately characterizing the dielectric materials' properties under nonlinear region: the relative permittivity and loss tangent using a cylindrical cavity resonator operating in TM010 mode. The proposed technique provides a complete characterization of the dielectric sample in both conventional (linear) and extended (non-linear) regions of the cavity perturbation. The NN model underwent training utilizing Levenberg Marquardt algorithm with a comprehensive dataset of approximately 4 million samples containing resonant frequency, quality factor, 3-dB bandwidth, insertion loss, reflection loss. This dataset was derived from the cylindrical cavity model, which incorporated diverse dielectric samples exhibiting varying relative permittivity and loss factor, all for a given sample thickness. Subsequently, the model underwent validation to assess its accuracy across a broad range of dielectric constants, loss tangent, and pragmatic thickness of the sample. The proposed model achieved an accuracy exceeding 99 %, underscoring its effectiveness in predicting the dielectric properties of different materials.

Original languageEnglish
Title of host publication2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350328264
DOIs
Publication statusPublished - 2023
Event2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023 - Ahmedabad, India
Duration: Dec 11 2023Dec 14 2023

Publication series

Name2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

Conference

Conference2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023
Country/TerritoryIndia
CityAhmedabad
Period12/11/2312/14/23

Keywords

  • Cylindrical cavity
  • neural networks
  • non-linearity
  • resonant technique

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Instrumentation
  • Radiation

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