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    Area of Science:

    • Optics
    • Data Storage
    • Machine Learning

    Background:

    • Volume holographic gratings enable high-density multiplexed recording.
    • Precise data reading in holographic storage necessitates strict Bragg matching of reading light.

    Purpose of the Study:

    • Investigate optical axis deviation tolerance of the recording objective lens during data reading in a collinear holographic data storage system.
    • Evaluate the impact of these deviations on key performance metrics.
    • Propose and validate a deep learning-based correction method for storage medium tilt.

    Main Methods:

    • Analyzed normalized diffraction intensity, signal-to-noise ratio, and bit error rate for reconstructed data pages.
    • Determined shift multiplexing interval, servo precision, and storage medium tilt angle tolerance.
    • Implemented a deep learning algorithm to correct for medium tilt.

    Main Results:

    • Shift multiplexing interval must exceed 2 µm.
    • Servo precision requires maintenance within ±200 nm.
    • Uncorrected system exhibits a 1.3° tilt angle tolerance; corrected system achieves 3° tolerance.

    Conclusions:

    • Findings offer critical data for enhancing amplitude-modulated collinear holographic data storage performance.
    • Results guide the development of specialized holographic storage servo systems.
    • Deep learning effectively mitigates the impact of storage medium tilt, significantly improving system tolerance.