TY - GEN
T1 - Applying Neural Network for Extending Cavity Perturbation
T2 - 2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023
AU - Khusro, Ahmad
AU - Akhter, Zubair
AU - Shamim, Atif
AU - Jha, Abhishek K.
AU - Hashmi, Mohammad S.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Cylindrical cavity
KW - neural networks
KW - non-linearity
KW - resonant technique
UR - http://www.scopus.com/inward/record.url?scp=85190367518&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190367518&partnerID=8YFLogxK
U2 - 10.1109/MAPCON58678.2023.10464098
DO - 10.1109/MAPCON58678.2023.10464098
M3 - Conference contribution
AN - SCOPUS:85190367518
T3 - 2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023
BT - 2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2023 through 14 December 2023
ER -