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AI Algorithm for Mode Classification of PCF-SPR Sensor Design

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Abstract

Photonic crystal fiber design based on surface plasmon resonance phenomenon (PCF-SPR) is optimized before it is fabricated for a particular application. An artificial intelligence algorithm is evaluated here to increase the ease of the simulation process for common users. COMSOL™ MultiPhysics is used. The algorithm suggests best among eight standard machine learning and one deep learning model to automatically select the desired mode, chosen visually by the experts otherwise. A total seven performance indices: namely, precision, recall, accuracy, F1-score, specificity, and Matthew correlation coefficient, are utilized to make the optimal decision. Robustness toward variations in sensor geometry design is also considered an optimal parameter. Several PCF-SPR-based photonic sensor designs are tested, and a large range optimal (based on phase matching) design is proposed. For this design algorithm has selected support vector machine (SVM) as the best option with an accuracy of 96%, F1-score is 95.83%, and MCC of 92.30%. The average sensitivity of the proposed sensor design with respect to change in refractive index (1.37–1.41) is 5500 nm/RIU. Resolution is 2.0498 \(\times {10}^{-5}\) \({\mathrm{RIU}}^{-1}\). The algorithm can be integrated into commercial software as an add-on or as a module in academic codes. The proposed novel step has saved approximately 75 min in the overall design process. The present work is equally applicable for mode selection of sensor other than PCF-SPR sensing geometries.

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Acknowledgements

We would like to acknowledge Prof. Sachin K. Srivastava for inspiring Ms. Prasunika to initiate work in this direction via his coursework.

Funding

This work is partially supported by GRANT Code I.M.P./2018/001045 by IMPRINT-II by S.E.R.B., Government of India.

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Authors and Affiliations

  1. Divyadrishti Laboratory, Department of Physics, IIT Roorkee, Roorkee, India

    Mayank Goswami, Prasunika Khare & Snehlata Shakya

  2. Department of Physics, IIT Roorkee, Roorkee, India

    Mayank Goswami, Prasunika Khare & Snehlata Shakya

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  1. Mayank Goswami
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  2. Prasunika Khare
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  3. Snehlata Shakya
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Contributions

Mayank Goswami: conceptualization, methodology, investigation, writing, visualization, supervision, and funding acquisition. Prasunika Khare: software and methodology, Snehlata Shakya: software verification, and re-investigation.

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Correspondence to Mayank Goswami.

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Goswami, M., Khare, P. & Shakya, S. AI Algorithm for Mode Classification of PCF-SPR Sensor Design. Plasmonics 19, 363–377 (2024). https://doi.org/10.1007/s11468-023-01997-5

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