Accurate prediction of geometrical parameters of an ultra-broadband metamaterial absorber using machine learning

In this paper, we systematically demonstrate the design and analysis of a new type of ultra-broadband tunable metamaterial perfect absorber (MPA) comprising a top vanadium dioxide (VO2) based patterned resonating patch, a continuous metallic film at the bottom, and an intermediate dielectric substrate having a thickness of only 0.18 $$\lambda$$ at the center working frequency. The simulation results reveal that the absorber achieves a bandwidth of 7.26 THz, ranging from 5.40 THz to 12.66 THz, with more than 90% absorptance and an average absorption of 98.21% under normal incidence of the incoming THz wave. Furthermore, absorptance exceeding 99% is achieved between 6.25 THz and 11.30 THz (5.05 THz bandwidth), demonstrating superior performance compared to existing broadband absorbers. The high absorption efficiency is attributed to the electric and magnetic dipole resonance, as illustrated through the electric field and vector surface current distribution at different frequencies. An equivalent RLC circuit model is developed using the least squares method, showing strong agreement with full-wave numerical simulations. However, designing metamaterial absorber requires extensive analysis of absorption spectra across a broad range of structural parameters – a computationally expensive process due to the complex interplay of impedance matching and electric field coupling. To overcome this challenge, we introduce a machine learning (ML)-based approach utilizing the Random Forest (RF) algorithm to predict absorption bandwidth and optimize structural parameters, significantly reducing computational time and spectral analyses. The RF model achieves considerably high accuracy, predicting an ultra-broadband absorption bandwidth of 7.26 THz with minimal error. We show that predicted and simulated results show excellent agreement, with negligible deviations. In addition, the terahertz absorber stably maintains more than 90% absorptance for both transverse electric (TE) and transverse magnetic (TM) waves up to 50° and due to its rotationally symmetric structure the proposed absorber is easy to fabricate and ensures complete polarization insensitivity. With its strong performance, the proposed MPA offers considerable potential for applications in terahertz modulation, switching, imaging, and biochemical sensing.

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Publication Details

Journal
Scientific Reports
Published
2025-12-22
DOI
https://doi.org/10.1038/s41598-025-27928-4
Citations
1
Primary Topic
Metamaterials and Metasurfaces Applications
Type
article
Field-Weighted Citation Impact
0.43

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article

Accurate prediction of geometrical parameters of an ultra-broadband metamaterial absorber using machine learning

Md. Samiul Habib, Oishi Jyoti, Md. Selim Habib, Mohammod Abdul Motin et al.
1 citations
Scientific Reports
Metamaterials and Metasurfaces Applications
0.43
article

Accurate prediction of geometrical parameters of an ultra-broadband metamaterial absorber using machine learning

Md. Samiul Habib, Oishi Jyoti, Md. Selim Habib, Mohammod Abdul Motin, Pritu Parna Sarkar, Md. Rezwan Ahmed
article en
1 citations

Abstract

In this paper, we systematically demonstrate the design and analysis of a new type of ultra-broadband tunable metamaterial perfect absorber (MPA) comprising a top vanadium dioxide (VO2) based patterned resonating patch, a continuous metallic film at the bottom, and an intermediate dielectric substrate having a thickness of only 0.18 $$\lambda$$ at the center working frequency. The simulation results reveal that the absorber achieves a bandwidth of 7.26 THz, ranging from 5.40 THz to 12.66 THz, with more than 90% absorptance and an average absorption of 98.21% under normal incidence of the incoming THz wave. Furthermore, absorptance exceeding 99% is achieved between 6.25 THz and 11.30 THz (5.05 THz bandwidth), demonstrating superior performance compared to existing broadband absorbers. The high absorption efficiency is attributed to the electric and magnetic dipole resonance, as illustrated through the electric field and vector surface current distribution at different frequencies. An equivalent RLC circuit model is developed using the least squares method, showing strong agreement with full-wave numerical simulations. However, designing metamaterial absorber requires extensive analysis of absorption spectra across a broad range of structural parameters – a computationally expensive process due to the complex interplay of impedance matching and electric field coupling. To overcome this challenge, we introduce a machine learning (ML)-based approach utilizing the Random Forest (RF) algorithm to predict absorption bandwidth and optimize structural parameters, significantly reducing computational time and spectral analyses. The RF model achieves considerably high accuracy, predicting an ultra-broadband absorption bandwidth of 7.26 THz with minimal error. We show that predicted and simulated results show excellent agreement, with negligible deviations. In addition, the terahertz absorber stably maintains more than 90% absorptance for both transverse electric (TE) and transverse magnetic (TM) waves up to 50° and due to its rotationally symmetric structure the proposed absorber is easy to fabricate and ensures complete polarization insensitivity. With its strong performance, the proposed MPA offers considerable potential for applications in terahertz modulation, switching, imaging, and biochemical sensing.

Scientific ReportsVol. 15(1)
Florida Institute of Technology (US), The University of Texas Rio Grande Valley (US), Bangladesh University of Business and Technology (BD), Rajshahi University of Engineering and Technology (BD), University of Rajshahi (BD)
National Aeronautics and Space Administration, Nuclear Safety and Security Commission
Openalex Percentile: Top 32%
Metamaterials and Metasurfaces Applications
0.43
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