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

ABSTRACT 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 (R ² ) of 0.9986 across TC‐50, TC‐60, and TC‐40 datasets, indicating reliable prediction of the simulated absorber response.