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Power, sensitivity, and response time optimization in TDM self-reference intensity sensor networks with ring

Salvador Vargas, Carmen Vazquez

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    Summary

    This study presents design rules for optimizing intensity sensors in ring resonators for frequency-based self-referencing. We introduce a method to balance sensor sensitivity and response time for improved signal quality in sensor networks.

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    Area of Science:

    • Photonics and Optical Engineering
    • Sensor Technology
    • Signal Processing

    Background:

    • Intensity sensors in ring resonators (RR) are crucial for frequency-based self-referencing.
    • Time division multiplexing sensor networks necessitate time-response analysis for self-reference sensors.
    • Optimizing sensor performance requires understanding the influence of coupling coefficients and losses.

    Purpose of the Study:

    • To establish design rules for optimizing the response of intensity sensors within ring resonators.
    • To analyze the time response requirements for self-reference sensors in multiplexed networks.
    • To develop a method for balancing sensor sensitivity and response speed.

    Main Methods:

    • Utilizing a frequency-based self-referencing method with sensors embedded in ring resonators.
    • Selecting measurement and reference frequencies as multiples of the RR Free Spectral Range.
    • Developing a weighting function to model the trade-off between sensitivity and response time based on coupling coefficient (K) and ring losses.

    Main Results:

    • Design rules for optimizing intensity sensor response in ring resonators are reported.
    • The use of specific frequencies enhances signal-to-noise ratio and mitigates coupling coefficient tolerance effects.
    • A weighting function is provided to define the optimal operational point considering K and ring losses.

    Conclusions:

    • The presented design rules and methods enable optimized performance of intensity sensors in ring resonator-based self-referencing systems.
    • The approach improves signal quality and reduces sensitivity to parameter variations.
    • This work provides a framework for designing efficient sensor networks with enhanced self-referencing capabilities.