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Machine learning-based inverse design of raised cosine few mode fiber for low coupling

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Abstract

In mode division multiplexing techniques, few-mode fiber (FMF) with multiple modes and low coupling is the demanded design. The design of FMF become more complex due to different parameters and shape design of it profile especially when you need to get a design with high number of modes, and it will consume a lot of computing time. Therefore this paper propose a machine learning technique employ neural network to design FMF raised cosine profile inversely. We realize the inverted design of raised cosine with U refractive index layer FMFs for supporting 6 mode operation, by adopting the minimal index difference between neighboring modes to get low coupling between modes. This proposed strategy gives higher precision with less complex design of FMF, in addition to low coupling between propagating modes. This promoting method can be applied in other types of fiber optic profile which it need a lot of parameters optimization.

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Acknowledgements

This research has been funded by Scientific Research Deanship at University of Ha’il – Saudi Arabia through project number RG-23 053.

Funding

Scientific Research Deanship at University of Ha’il – Saudi Arabia.

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Authors and Affiliations

  1. Department of Physics, College of Science, University of Ha’il, P.O.Box 2440, Ha’il, Saudi Arabia

    Saleh Chebaane

  2. Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia

    Saleh Chebaane

  3. Department of Physics, College of Science and Arts at Ar-Rass, Qassim University, Ar Rass, Saudi Arabia

    Sana Ben Khalifa

  4. Laboratory of Energy and Materials (LabEM), ESSTHS, University of Sousse, 4011 H, Sousse, Tunisia

    Sana Ben Khalifa

  5. Department of Computer Science and Engineering, Applied College, University of Ha’il, Ha’il, Saudi Arabia

    Maher Jebali

  6. Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

    Ali Louati

  7. UGA University of Grenoble Alpes and Member in GIPSA Laboratory, Grenoble, France

    Haythem Bahri

  8. Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia

    Alaa Dafhalla

Authors
  1. Saleh Chebaane
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  2. Sana Ben Khalifa
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  3. Maher Jebali
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  4. Ali Louati
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  5. Haythem Bahri
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  6. Alaa Dafhalla
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Contributions

Authors' contributions - SC: Performed the analysis; Wrote the paper. -SBK: Conceived and designed the analysis, Wrote the paper. -MJ:Collected the data, Performed the analysis. -AL: Collected the data, analysis tools. - HB: Collected the data - AD: Performed the analysis

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Correspondence to Saleh Chebaane.

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Chebaane, S., Ben Khalifa, S., Jebali, M. et al. Machine learning-based inverse design of raised cosine few mode fiber for low coupling. Opt Quant Electron 56, 56 (2024). https://doi.org/10.1007/s11082-023-05695-8

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  • DOI: https://doi.org/10.1007/s11082-023-05695-8

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