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Optical-electronic hybrid Fourier convolutional neural network based on super-pixel complex-valued modulation.

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    This study introduces a novel optical-electronic hybrid convolutional neural network (CNN) for real-time optical computing. The proposed complex-valued modulation method achieves high classification accuracy on benchmark datasets.

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    Area of Science:

    • Optoelectronics
    • Computer Science
    • Artificial Intelligence

    Background:

    • Optical computing offers potential for high-speed parallel processing.
    • Hybrid optical-electronic systems combine the strengths of both domains.
    • Convolutional Neural Networks (CNNs) are crucial for image recognition tasks.

    Purpose of the Study:

    • To propose and investigate an optical-electronic hybrid CNN system.
    • To develop a practical complex-valued modulation method for optical CNNs.
    • To evaluate the system's performance in classification tasks.

    Main Methods:

    • A complex-valued modulation method using a liquid-crystal-on-silicon spatial light modulator and a diffractive optical element was developed.
    • The hybrid CNN model with one convolutional layer was trained electrically.
    • Performance was evaluated using MNIST, Fashion MNIST, and Cifar10 datasets.

    Main Results:

    • The proposed complex-valued modulation method demonstrated feasibility through Fourier plane convolution comparisons.
    • The hybrid CNN achieved high classification accuracies: 97.55% (MNIST), 88.81% (Fashion MNIST), and 56.16% (Cifar10).
    • Performance surpassed models using only amplitude or phase modulation and neared ideal complex-valued modulation.

    Conclusions:

    • The developed optical-electronic hybrid CNN system is robust and capable of parallel processing for real-time optical computing.
    • The proposed complex-valued modulation method is effective and offers significant advantages over amplitude-only or phase-only methods.
    • This approach represents a practical advancement in optical machine learning implementations.