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    We developed a new training method for 3D-fabricated optical processors. This approach accounts for manufacturing limits, creating robust diffractive networks with quantized weights for high-speed computation.

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    Area of Science:

    • Photonics and optical computing
    • Micro-fabrication and nanophotonics
    • Machine learning for physical systems

    Background:

    • Optical processors offer high-speed, high-dimensional linear operations.
    • 3D micro-fabrication enables complex optical processor designs.
    • Limited precision in fabrication leads to model mismatch due to quantized optical weights.

    Purpose of the Study:

    • To propose a quantization-aware training framework for 3D-fabricated optical processors.
    • To address the model mismatch caused by quantized optical weights in diffractive networks.
    • To enable the design of robust optical processors despite fabrication constraints.

    Main Methods:

    • Developed a quantization-aware training framework.
    • Integrated physical constraints of micro-fabrication into the training process.
    • Numerically demonstrated the framework using diffractive networks.

    Main Results:

    • Designed state-of-the-art optical processors using diffractive networks.
    • Achieved robust designs despite quantized learnable parameters.
    • Validated the approach for multiple computational tasks.

    Conclusions:

    • The proposed framework enables robust 3D-fabrication of optical processors.
    • Quantization-aware training overcomes precision limitations in micro-fabrication.
    • Lays the foundation for future advancements in optical computing hardware.