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    Researchers developed a novel optical multiplication and division method using orbital angular momentum (OAM) modes and optical diffractive neural networks (ODNNs). This breakthrough enables high-purity optical computing operations, advancing digital optical computing architectures.

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    Area of Science:

    • Optics and Photonics
    • Computational Science
    • Artificial Intelligence

    Background:

    • Optical digital computing offers high-speed, efficient, and precise information processing for AI and communication.
    • Key challenges in optical computing include developing effective computational dimensions and precise control for multiplication/division operations.

    Purpose of the Study:

    • To propose and demonstrate a fixed-base multiplication and division scheme using orbital angular momentum (OAM) modes and optical diffractive neural networks (ODNNs).
    • To overcome limitations in optical multiplication/division by utilizing OAM modes as the computational physical dimension.

    Main Methods:

    • Employed OAM modes as the computational physical dimension within an optical system.
    • Utilized ODNNs to perform mode-parallel transformations for numerical shifts, enabling multiplication and division.
    • Constructed a 3-layer ODNN to realize fixed-base multiplication and division for n=1, 2, and 3.

    Main Results:

    • Achieved fixed-base multiplication and division operations with OAM modes, reaching 99% mode purity in outputs.
    • Demonstrated dynamic switching between multiplication and division within the same system via phase matrix rotation.
    • Successfully implemented the scheme for n=1, 2, and 3.

    Conclusions:

    • The proposed OAM-mode-based ODNN scheme provides a feasible pathway for fixed-base optical multiplication and division.
    • This research offers valuable insights for the development of future digital optical computing architectures.
    • The method enhances the capabilities of optical computing for complex arithmetic operations.