Performance Analysis and Optimization of Terahertz Metamaterial Absorbers Using Machine Learning-Based Inverse Modeling

We present a tunable broadband terahertz (THz) metamaterial absorber with a structurally simple, single-layered vanadium dioxide (VO2) elliptical ring resonator. This design achieves a wide, near-perfect absorption band (3.5–5 THz) without the need for complex multi-layer stacks or hybrid-patterned alternatives. Full-wave simulations demonstrate that VO2’s insulator-to-metal transition dynamically enhances absorption, while structural parameters—ring width, ellipticity ratio, and dielectric thickness—precisely control bandwidth and spectral response, as explained by impedance matching theory and electric field distributions. Furthermore, we explore the impact of varying the angle of incidence, highlighting the angular sensitivity of the structure. Beyond conventional parametric sweeps, we implement a targeted machine learning (ML) strategy for inverse design. Our models, trained on augmented data, show that Random Forest Regressor excels in predicting multiple geometric parameters simultaneously, while CatBoost is optimal for single-target prediction. The predicted geometric parameters are validated through simulation; this ML-guided approach, tailored to different design goals, combines physics-based modeling with data-driven optimization, offering a robust and efficient framework for designing next-generation broadband THz absorbers.