Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization
This research presents a novel square-shaped photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor, designed using the external metal deposition (EMD) technique, for highly sensitive refractive index (RI) sensing applications. The proposed sensor operates effectively over an RI r...
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MDPI AG
2025-06-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/6/565 |
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| author | Ayushman Ramola Amit Kumar Shakya Arik Bergman |
| author_facet | Ayushman Ramola Amit Kumar Shakya Arik Bergman |
| author_sort | Ayushman Ramola |
| collection | DOAJ |
| description | This research presents a novel square-shaped photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor, designed using the external metal deposition (EMD) technique, for highly sensitive refractive index (RI) sensing applications. The proposed sensor operates effectively over an RI range of 1.33 to 1.37 and supports both x- polarized and y-polarized modes. It achieves a wavelength sensitivity of 15,800 nm/RIU and 14,300 nm/RIU, and amplitude sensitivities of 11,584 RIU<sup>−1</sup> and 11,007 RIU<sup>−1</sup>, respectively, for the x-pol. and y-pol. The sensor also reports a resolution in the order of 10<sup>−6</sup> RIU and a strong linearity of R<sup>2</sup> ≈ 0.97 for both polarization modes, indicating its potential for precision detection in complex sensing environments. Beyond the sensor’s structural and performance innovations, this work also explores the future integration of artificial intelligence (AI) into PCF-SPR sensor design. AI techniques such as machine learning and deep learning offer new pathways for sensor calibration, material optimization, and real-time adaptability, significantly enhancing sensor performance and reliability. The convergence of AI with photonic sensing not only opens doors to smart, self-calibrating platforms but also establishes a foundation for next-generation sensors capable of operating in dynamic and remote applications. |
| format | Article |
| id | doaj-art-79d65297fa5d4f99aa310385db9b568a |
| institution | OA Journals |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-79d65297fa5d4f99aa310385db9b568a2025-08-20T02:21:53ZengMDPI AGPhotonics2304-67322025-06-0112656510.3390/photonics12060565Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor OptimizationAyushman Ramola0Amit Kumar Shakya1Arik Bergman2Department of Electrical and Electronics Engineering, Ariel University, Ariel 40700, IsraelDepartment of Electrical and Electronics Engineering, Ariel University, Ariel 40700, IsraelDepartment of Electrical and Electronics Engineering, Ariel University, Ariel 40700, IsraelThis research presents a novel square-shaped photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor, designed using the external metal deposition (EMD) technique, for highly sensitive refractive index (RI) sensing applications. The proposed sensor operates effectively over an RI range of 1.33 to 1.37 and supports both x- polarized and y-polarized modes. It achieves a wavelength sensitivity of 15,800 nm/RIU and 14,300 nm/RIU, and amplitude sensitivities of 11,584 RIU<sup>−1</sup> and 11,007 RIU<sup>−1</sup>, respectively, for the x-pol. and y-pol. The sensor also reports a resolution in the order of 10<sup>−6</sup> RIU and a strong linearity of R<sup>2</sup> ≈ 0.97 for both polarization modes, indicating its potential for precision detection in complex sensing environments. Beyond the sensor’s structural and performance innovations, this work also explores the future integration of artificial intelligence (AI) into PCF-SPR sensor design. AI techniques such as machine learning and deep learning offer new pathways for sensor calibration, material optimization, and real-time adaptability, significantly enhancing sensor performance and reliability. The convergence of AI with photonic sensing not only opens doors to smart, self-calibrating platforms but also establishes a foundation for next-generation sensors capable of operating in dynamic and remote applications.https://www.mdpi.com/2304-6732/12/6/565photonic crystal fibersurface plasmon resonanceexternal metal depositionrefractive indexartificial intelligencemachine learning |
| spellingShingle | Ayushman Ramola Amit Kumar Shakya Arik Bergman Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization Photonics photonic crystal fiber surface plasmon resonance external metal deposition refractive index artificial intelligence machine learning |
| title | Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization |
| title_full | Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization |
| title_fullStr | Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization |
| title_full_unstemmed | Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization |
| title_short | Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO<sub>2</sub> Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization |
| title_sort | finite element method based modeling of a novel square photonic crystal fiber surface plasmon resonance sensor with a au tio sub 2 sub interface and the relevance of artificial intelligence techniques in sensor optimization |
| topic | photonic crystal fiber surface plasmon resonance external metal deposition refractive index artificial intelligence machine learning |
| url | https://www.mdpi.com/2304-6732/12/6/565 |
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