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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Updated: Aug 26, 2025

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Training optronic convolutional neural networks on an optical system through backpropagation algorithms.

Ziyu Gu, Zicheng Huang, Yesheng Gao

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    Summary

    This study introduces in-situ training for optical neural networks, enabling direct optical computation for both training and inference. This approach accelerates big data processing and enhances the robustness of optronic convolutional neural networks (OPCNN).

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    Area of Science:

    • Optics
    • Artificial Intelligence
    • Computer Science

    Background:

    • Optical neural networks (ONNs) offer potential for faster big data processing.
    • Current ONNs often use electronic training and optical inference, limiting computational advantages.
    • Optronic convolutional neural networks (OPCNNs) still require significant computational resources during training.

    Purpose of the Study:

    • To develop an in-situ training algorithm for direct optical training of neural networks.
    • To leverage optical computing for both forward and backward propagation in OPCNNs.
    • To enhance the practicality and application range of OPCNNs.

    Main Methods:

    • Derived backpropagation algorithms for OPCNNs to enable optical gradient calculation.
    • Implemented in-situ training where both forward and backward propagation occur on the same optical system.
    • Introduced optical nonlinearity using photorefractive crystal SBN:60 and derived its backpropagation algorithm.

    Main Results:

    • Numerical simulations validated the feasibility of the proposed in-situ training algorithms for classification tasks.
    • In-situ training eliminated performance degradation caused by training-inference platform inconsistencies.
    • OPCNNs trained optically demonstrated strong robustness against misalignment, expanding practical applications.

    Conclusions:

    • In-situ training significantly enhances the efficiency and robustness of optical neural networks.
    • Direct optical computation for training and inference unlocks the full potential of optical computing for big data.
    • The proposed methods pave the way for more practical and widely applicable optical computing solutions.