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    This study introduces a data-driven framework using bidirectional long short-term memory (Bi-LSTM) networks to emulate high-speed optical interconnects. The novel approach significantly reduces computation time for designing and optimizing short-reach optical links.

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    Area of Science:

    • Optoelectronics
    • Machine Learning
    • Optical Communications

    Background:

    • High-speed optical interconnects are crucial for data centers.
    • Conventional models for VCSEL-based PAM-4 systems are computationally intensive.
    • Accurate emulation is needed for efficient system design.

    Purpose of the Study:

    • To develop a data-driven emulation framework for VCSEL-based PAM-4 optical interconnects.
    • To overcome the limitations of traditional rate-equation models.
    • To accelerate the design and optimization of short-reach optical links.

    Main Methods:

    • Utilized experimental waveforms to train bidirectional long short-term memory (Bi-LSTM) networks.
    • Employed transfer learning and weight interpolation for model generalization.
    • Compared emulation performance against conventional rate-equation models.

    Main Results:

    • Achieved a data-driven emulation framework for VCSEL-based PAM-4 systems.
    • Reduced computation time by 20-fold compared to independent training.
    • Maintained a normalized mean squared error below 0.04.
    • Demonstrated effective extension to new operating regimes.

    Conclusions:

    • The Bi-LSTM based emulator offers a rapid and accurate tool for optical interconnect design.
    • This data-driven approach significantly enhances the efficiency of optical link optimization.
    • The framework provides a viable alternative to computationally expensive traditional models.