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Plasmonic sensor using generative adversarial networks integration.

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    This summary is machine-generated.

    This study integrates machine learning (ML) with photonic crystal fiber (PCF) plasmonic sensors to boost performance. The novel sensor design achieves high sensitivity and resolution for refractive index sensing, applicable to chemical and medical diagnostics.

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    Area of Science:

    • Photonics
    • Plasmonics
    • Sensing Technologies
    • Machine Learning

    Background:

    • Machine learning (ML) significantly enhances sensing technologies across diverse fields.
    • Photonic crystal fibers (PCFs) offer unique structures for advanced sensing applications.
    • Plasmonic sensing utilizes surface plasmon resonance for high-sensitivity detection.

    Purpose of the Study:

    • To integrate ML techniques with PCF-based plasmonic sensors to improve sensor performance.
    • To investigate a novel PCF structure with open channels for enhanced mode coupling and analyte interaction.
    • To develop an ML model, enhanced by generative adversarial networks (GANs), for accurate confinement loss prediction.

    Main Methods:

    • Utilizing a PCF with two open channels to facilitate analyte interaction and mode coupling.
    • Incorporating a thin gold layer within the PCF channels to generate surface plasmons.
    • Employing generative adversarial networks (GANs) to augment training data for an artificial neural network (ANN) model.

    Main Results:

    • Achieved maximum wavelength sensitivity of 9000 nm/RIU and amplitude sensitivity of 490.41 RIU-1.
    • Demonstrated a high resolution of 1.11 × 10-5 RIU and a maximum figure-of-merit (FOM) of 138.04 RIU-1.
    • The ML model, enhanced by GANs, accurately predicted confinement loss for various analytes and wavelengths.

    Conclusions:

    • The integrated ML and PCF plasmonic sensor offers superior performance for refractive index sensing.
    • The sensor's design and ML approach enable precise detection within the RI range of 1.33 to 1.40.
    • This versatile sensing platform is suitable for critical applications in chemical sensing and medical diagnostics.