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Related Experiment Video

Updated: Jan 8, 2026

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Published on: December 27, 2012

ML/GA-based performance optimization of PBG-enhanced THz microstrip patch antennas on PTFE-SWCNT.

Samir Brahim Belhaouari¹, Allel Mokaddem^2,3, Djamila Ziani^4,5

¹Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar. sbelhaouari@hbku.edu.qa.

Scientific Reports

|December 18, 2025

Summary

This study optimized a terahertz (THz) antenna using photonic bandgap (PBG) structures and machine learning. The novel design achieves high performance, showing potential for medical imaging and biosensing applications.

Keywords:

Biomedical applications CST simulation Genetic algorithm Machine learning Neural networks Photonic bandgap (PBG) structures Polytetrafluoroethylene (PTFE)Sing-Wall carbon nanotubes (SWCNTs)Terahertz (THz) antenna

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Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics

Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics

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Last Updated: Jan 8, 2026

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Published on: December 27, 2012

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Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics

Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics

Published on: May 28, 2016

Area of Science:

Electromagnetics and Applied Physics
Materials Science and Engineering
Computational Intelligence

Background:

Terahertz (THz) technology demands efficient antennas for applications like medical imaging and biosensing.
Photonic Bandgap (PBG) structures can enhance antenna performance by controlling electromagnetic wave propagation.
Optimizing antenna design requires balancing complex physical parameters for desired radiation characteristics.

Purpose of the Study:

To design and optimize a THz microstrip patch antenna integrated with PBG structures.
To leverage advanced materials like Polytetrafluoroethylene (PTFE) and Single-Wall Carbon Nanotubes (SWCNTs) for improved antenna efficiency.
To develop a rapid and accurate antenna design methodology using electromagnetic simulation, machine learning, and evolutionary optimization.

Main Methods:

Electromagnetic simulations using CST software to model antenna performance.
Training machine learning models (Linear Regression, K-Nearest Neighbors, Decision Trees, Neural Networks) on simulated data.
Utilizing a genetic algorithm for optimizing antenna geometry based on performance metrics.
Conducting Specific Absorption Rate (SAR) analysis to ensure safety compliance.

Main Results:

The neural network model achieved high predictive accuracy (R² > 0.94) for key antenna parameters.
Optimized antenna geometry yielded a 36.8 GHz bandwidth, 9.4 dBi gain, 93.7% efficiency, and -26.1 dB return loss.
SAR analysis confirmed the antenna operates within FCC safety limits (max 1.4 W/kg).

Conclusions:

The integrated approach of simulation, machine learning, and genetic algorithms offers a faster and more accurate antenna design process.
The proposed PBG-enhanced THz antenna demonstrates excellent performance, compactness, and material flexibility.
This antenna is well-suited for advanced applications in non-invasive medical imaging, biosensing, and wearable health monitoring.