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    Area of Science:

    • Photonics
    • Optical Signal Processing
    • Integrated Optics

    Background:

    • The fractional Fourier transform (FrFT) is vital for signal processing, but conventional methods lack reconfigurability and integrability.
    • Existing implementations using lens systems or fiber arrays are bulky and difficult to adapt.

    Purpose of the Study:

    • To present a novel programmable discrete fractional Fourier transform (DFrFT) processor.
    • To overcome limitations of conventional FrFT implementations by enabling dynamic reconfigurability and integration.

    Main Methods:

    • Designed a processor using a fixed array of basic transformation units (BTUs) with a dynamically reconfigurable architecture.
    • Leveraged DFrFT order additivity to synthesize arbitrary DFrFT matrices.
    • Employed inverse design to create four BTUs (π/8, π/4, π/2, π) with high simulated fidelity (>0.995).

    Main Results:

    • Achieved high fidelity (>0.989) for assembled DFrFT matrices across 16 transformation orders in numerical analysis.
    • Experimental results on a silicon photonic platform showed individual BTU fidelity (>0.85) and assembled DFrFT fidelity (>0.8).
    • Confirmed modularity, reconfigurability, and robustness through order mapping and fabrication tolerance analysis.

    Conclusions:

    • The developed processor offers a scalable and programmable platform for integrated optical signal processing.
    • This technology is particularly suitable for time-frequency transformations and non-stationary signal analysis in optical computing.
    • The system demonstrates significant potential for advancing future optical computing systems.