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Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system.

Xing Xing Guo, Shui Ying Xiang, Ya Hui Zhang

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    Summary

    A new four-channel reservoir computing system using mutually coupled vertical cavity surface emitting lasers (MDC-VCSELs) was developed. This system achieves comparable prediction performance to single-channel systems while increasing information processing rates fourfold.

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    Area of Science:

    • Photonics
    • Neuromorphic Computing
    • Laser Physics

    Background:

    • Reservoir computing (RC) offers a powerful framework for complex time-series processing.
    • Vertical cavity surface emitting lasers (VCSELs) are key components in photonic systems due to their scalability and integration potential.
    • Polarization dynamics in lasers provide a rich platform for developing novel computing paradigms.

    Purpose of the Study:

    • To propose and numerically demonstrate a novel four-channel reservoir computing system.
    • To leverage polarization dynamics in mutually coupled VCSELs (MDC-VCSELs) for enhanced information processing.
    • To evaluate the performance of this four-channel system for chaotic time-series prediction.

    Main Methods:

    • Utilizing two orthogonally polarized modes (x and y) of two MDC-VCSELs to create four distinct computational channels.
    • Implementing a chaotic time-series prediction task to quantitatively assess system performance.
    • Systematically analyzing the impact of various parameters, including injection current, coupling strength, and frequency detuning.

    Main Results:

    • The four-channel RC system demonstrates prediction performance comparable to single-channel RC systems.
    • A fourfold increase in information processing rate is achievable with the proposed four-channel configuration.
    • The study identifies key parameters influencing prediction accuracy and system stability.

    Conclusions:

    • The proposed four-channel RC based on MDC-VCSELs offers a viable method for significantly enhancing information processing rates.
    • This approach holds promise for advancing the development of high-speed neuromorphic photonic systems.
    • Further research can explore optimizing parameters for even greater performance gains.