Machine Learning-Assisted Design and Performance Prediction of a Compact Dual-Band Polarization-Insensitive THz Metamaterial Absorber for Skin-Cancer-Related Refractive-Index Sensing

The proposed metamaterial absorber is built on a compact Quad-Arrow Split Ring Resonator (QASRR) consisting of a diamond-shaped structure with a centrally loaded split circular ring with four arrow-shaped extensions. The absorber exhibits polarization-insensitive dual-band absorption at 3.3 and 4.8 terahertz (THz), with peak absorptivities of 99.91% and 99.96%, respectively. Its normalized size is 0.24λ × 0.24λ × 0.03λ at the first resonance. Using representative refractive indices for normal and cancer-related skin-cell models (n = 1.360 and n = 1.380), the absorber resolves a small refractive-index contrast under a controlled homogeneous analyte model, with sensitivities of 2950 GHz/RIU and 4846 gigahertz per refractive index unit (GHz/RIU). The corresponding figure of merit (FOM) values are 32.13 and 21.5, with Quality-factors of 14.27 and 12.8. Field distributions, surface currents, substrate-dependent parametric studies, and equivalent circuit modeling are used to explain the dual-resonance mechanism and validate the results. The main contribution is a unified framework that links compact QASRR-based dual-band absorption, skin-cancer-related refractive-index sensing, field/current and circuit-based electromagnetic mechanism interpretation, and StackNet-assisted response prediction. StackNet is developed using physics-informed engineered features and compared with k-nearest neighbor (KNN), random forest (RF), extra trees (ET), and extreme gradient boosting (XGBoost) models. It achieves an average coefficient of determination (R²) of 0.9986 across TC-50, TC-60, and TC-40 datasets, indicating reliable prediction of the simulated absorber response.