Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing
4 affiliations
- 1Department of Electrical and Electronic Engineering International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh
- 2Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology (CUET) Chattogram Bangladesh
- 3Department of Computer Science and Engineering International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh
- 4Department of Physics International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh
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.
Related
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BibTeX
@article{nipun2026machine,
title = {{Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing}},
author = {Md. Murad Kabir Nipun and S. M. Rifatur Rana and Md Golam Morshed and Imrose Jahan and F. A. Sabbir Ahamed and Md. Moinul Islam},
journal = {Advanced Electronic Materials},
year = {2026},
month = jun,
doi = {10.1002/aelm.70449},
url = {https://doi.org/10.1002/aelm.70449},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing
AU - Md. Murad Kabir Nipun
AU - S. M. Rifatur Rana
AU - Md Golam Morshed
AU - Imrose Jahan
AU - F. A. Sabbir Ahamed
AU - Md. Moinul Islam
PY - 2026
DA - 2026/06/01
JO - Advanced Electronic Materials
DO - 10.1002/aelm.70449
UR - https://doi.org/10.1002/aelm.70449
AB - 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.
SN - 2199-160X
ER -