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

ABSTRACT: This study presents the design and optimization of a terahertz (THz) microstrip patch antenna enhanced with photonic bandgap (PBG) structures. The antenna is implemented on a Polytetrafluoroethylene (PTFE) substrate with Single-Wall Carbon Nanotube (SWCNT) conductors, leveraging the substrate's low loss tangent and stable permittivity together with the high conductivity of SWCNTs to improve radiation performance. Key physical parameters, including air gap side, lattice constant, and substrate thickness, were varied using CST simulations to generate a comprehensive dataset. Four machines learning models Linear Regression, K-Nearest Neighbors, Decision Trees, and Neural Networks were trained, with the neural network achieving the best predictive accuracy (R² > 0.94) and very low errors acros

SUBMITTER: Belhaouari SB 

PROVIDER: S-EPMC12714757 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature