Numerical Investigation of a Gear-Shaped Triple-Band Perfect Terahertz Metamaterial Absorber as Biochemical Sensor

Anik, M. Hussayeen Khan; Mahmud, Sakib; Mahmood, K.B.M. Sharif; Bokhtiar-Al-Zami,; Isti, M. Ifaz Ahmad; Talukder, Hriteshwar; Biswas, Shovasis Kumar

doi:10.1109/jsen.2022.3195657

Cited by 12 publications

(2 citation statements)

References 54 publications

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“…7b depicts the inverse relationship between the resonance frequency (f 4 ) and the refractive index. The sensitivity (S) is Δf/Δn (THz/ RIU) where Δn and Δf represent change in the refractive index and frequency, respectively [49][50][51] . The S values for frequency modes f 1 , f 2 , f 3 , f 4 , and f 5 were found to be 2.…”

Section: Resultsmentioning

confidence: 99%

Machine learning assisted hepta band THz metamaterial absorber for biomedical applications

Jain

Chhabra

Chauhan

et al. 2023

Sci Rep

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A hepta-band terahertz metamaterial absorber (MMA) with modified dual T-shaped resonators deposited on polyimide is presented for sensing applications. The proposed polarization sensitive MMA is ultra-thin (0.061 λ) and compact (0.21 λ) at its lowest operational frequency, with multiple absorption peaks at 1.89, 4.15, 5.32, 5.84, 7.04, 8.02, and 8.13 THz. The impedance matching theory and electric field distribution are investigated to understand the physical mechanism of hepta-band absorption. The sensing functionality is evaluated using a surrounding medium with a refractive index between 1 and 1.1, resulting in good Quality factor (Q) value of 117. The proposed sensor has the highest sensitivity of 4.72 THz/RIU for glucose detection. Extreme randomized tree (ERT) model is utilized to predict absorptivities for intermediate frequencies with unit cell dimensions, substrate thickness, angle variation, and refractive index values to reduce simulation time. The effectiveness of the ERT model in predicting absorption values is evaluated using the Adjusted R2 score, which is close to 1.0 for nmin = 2, demonstrating the prediction efficiency in various test cases. The experimental results show that 60% of simulation time and resources can be saved by simulating absorber design using the ERT model. The proposed MMA sensor with an ERT model has potential applications in biomedical fields such as bacterial infections, malaria, and other diseases.

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Section: Resultsmentioning

confidence: 99%

Machine learning assisted hepta band THz metamaterial absorber for biomedical applications

Jain

Chhabra

Chauhan

et al. 2023

Sci Rep

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show abstract

“…MM have also been explored using optical waveguides, optical buffers, slow light devices, optical sensors and detection, switching, opto-chemical sensors, thermo-optical modulators and cloaking devices etc. [92]- [95] The application of deep neural networks for optimizing the design of metamaterial structures has been recently investigated in a couple of studies for example the design of THz metamaterial absorbers have been reported in [96]. The design of chiral metamaterial induced asymmetrical transmission (AT) based on a deep learning approach has been investigated to accelerate chiral metamaterials design [97].…”

Section: A Deep Learning In Thz Metamaterialsmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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Following the recent progress in the development of Terahertz (THz) generation and detection, THz technology is being widely used to characterize test sample properties in various applications including nondestructive testing, security inspection and medical applications. In this paper, we have presented a broad review of the recent usage of artificial intelligence (AI) particularly, deep learning techniques in various THz sensing, imaging, and spectroscopic applications with emphasis on their implementation for medical imaging of cancerous cells. Initially, the fundamentals principles and techniques for THz generation and detection, imaging and spectroscopy are introduced. Subsequently, a brief overview of AI -machine learning and deep learning techniques is summarized, and their performance is compared. Further, the usage of deep learning algorithms in various THz applications is reported, with focus on metamaterials design and classification, detection, reconstruction, segmentation, parameter extraction and denoising tasks. Moreover, we also report the metrics used to evaluate the performance of deep learning models and finally, the existing research challenges in the application of deep learning in THz cancer imaging applications are identified and possible solutions are suggested through emerging trends. With the continuous increase of acquired THz data -sensing, spectral and imaging, artificial intelligence has emerged as a dominant paradigm for embedded data extraction, understanding, perception, decision making and analysis. Towards this end, the integration of state-of-the-art machine learning techniques such as deep learning with THz applications enable detailed computational and theoretical analysis for better validation and verification than modelling techniques that precede the era of machine learning. The study will facilitate the large-scale clinical applications of deep learning enabled THz imaging systems for the development of smart and connected next generation healthcare systems as well as provide a roadmap for future research direction.

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Theoretical analysis of InAs based Bi-tunable narrow band terahertz perfect absorber for thermal sensing application

Niharika,

Singh

2024

Micro and Nanostructures

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Numerical Investigation of a Gear-Shaped Triple-Band Perfect Terahertz Metamaterial Absorber as Biochemical Sensor

Cited by 12 publications

References 54 publications

Machine learning assisted hepta band THz metamaterial absorber for biomedical applications

Machine learning assisted hepta band THz metamaterial absorber for biomedical applications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Theoretical analysis of InAs based Bi-tunable narrow band terahertz perfect absorber for thermal sensing application

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