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Updated: Sep 11, 2025

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Joint loss function design in diffractive optical neural network classifiers for high power efficiency.

Mengguang Fan, Shuping Jin, Yinwei Gu

    Optics Express
    |August 13, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A novel joint loss function (J-SCE) significantly boosts the power efficiency of diffractive optical neural networks (DONNs) for image recognition. This advancement enhances DONN stability and practical application in computer vision tasks.

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    Area of Science:

    • Optics
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Diffractive optical neural networks (DONNs) offer high speed, wide bandwidth, and parallel processing for computer vision.
    • Existing DONN classifiers face limitations in power efficiency for practical applications.

    Purpose of the Study:

    • To introduce a joint loss function (J-SCE) to enhance DONN power efficiency and classification performance.
    • To improve the energy directing capabilities and robustness of DONN systems.

    Main Methods:

    • Development and implementation of a joint loss function (J-SCE) integrating classification accuracy and diffractive power efficiency.
    • Evaluation of the J-SCE function's impact on DONN power efficiency and classification accuracy.

    Main Results:

    • Achieved a significant improvement in DONN classifier power efficiency from 0.92% to 12.89%.
    • Maintained a high classification accuracy of 95.36% with the J-SCE function.
    • Demonstrated enhanced system robustness to noise and improved overall stability.

    Conclusions:

    • The J-SCE function effectively enhances DONN power efficiency by optimizing energy distribution.
    • This work represents a significant contribution towards the practical implementation of DONN classifiers in image recognition and information processing.