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Fabry-Perot interferometric sensor demodulation system utilizing multi-peak wavelength tracking and neural network

Shengchao Chen, Feifan Yao, Sufen Ren

    Optics Express
    |October 14, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a cost-effective absolute demodulation system for Fiber-optic Polarization Interferometry (FPI) sensors. The novel system utilizes an array waveguide grating and neural networks for high-precision, scalable, and repeatable FPI sensor measurements.

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    Area of Science:

    • Optical Engineering
    • Sensor Technology
    • Artificial Intelligence in Measurement

    Background:

    • Fiber-optic Polarization Interferometry (FPI) sensors require high performance, cost-effectiveness, stability, and scalability for engineering applications.
    • Achieving excellent performance often involves expensive equipment and complex algorithms, limiting practical adoption.

    Purpose of the Study:

    • To develop a novel, high-performance, and cost-effective absolute demodulation system for FPI sensors.
    • To overcome the limitations of existing FPI sensor demodulation techniques regarding cost and complexity.

    Main Methods:

    • Demultiplexing reflected light from FPI sensors into distinct channels using an array waveguide grating (AWG).
    • Translating interference spectrum features into transmitted intensity variations within each AWG channel.
    • Employing an end-to-end neural network model for simultaneous interrogation of absolute peak wavelengths and cavity length.

    Main Results:

    • The proposed system achieves high precision: up to ± 14 pm for multi-wavelength interrogation and ± 0.07 µm for cavity length interrogation.
    • The neural network method enables theoretical interrogation resolution at the picometer (pm) level.
    • Demonstrated outstanding demodulation repeatability and suitability for practical applications.

    Conclusions:

    • The developed system offers a high-performance, cost-effective, and reliable solution for FPI sensor demodulation.
    • The neural network approach enhances generalization capabilities without requiring large datasets, facilitated by data augmentation.
    • The system is well-suited for practical engineering measurements demanding precision and scalability.