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Nanoscale isolation layer design for non-volatile integrated photonics targeting high-energy efficiency programming

Hengyu Zhang, Hui Xu, Bing Song

    Optics Letters
    |April 1, 2025
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    This study presents a novel photonic in-memory computing device design. A nanoscale isolation layer slashes programming energy by over 80% and computing energy by over 35% for efficient AI processing.

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    Area of Science:

    • Photonics
    • Artificial Intelligence
    • Computer Engineering

    Background:

    • Rapid AI advancements necessitate low-latency, energy-efficient processing.
    • Photonic in-memory computing offers a promising solution for these demands.

    Purpose of the Study:

    • To introduce a novel non-volatile integrated photonic device design.
    • To significantly reduce both programming and computing energy consumption in photonic computing systems.

    Main Methods:

    • Integration of a nanoscale isolation layer into the photonic device.
    • Characterization of programming energy, precision, and endurance.
    • Assessment of insertion loss and computing energy reduction.

    Main Results:

    • Programming energy reduced by over 80% due to the isolation layer.
    • Achieved 6-bit programming precision with consistent performance over 1000 cycles.
    • Reduced insertion loss to 0.5 dB, decreasing computing energy by over 35%.

    Conclusions:

    • The novel device design offers substantial energy savings for photonic computing.
    • This advancement contributes to the development of high-energy efficiency and large-scale integrated photonic computing systems.