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Orthogonality of diffractive deep neural network.

Shuiqin Zheng, Shixiang Xu, Dianyuan Fan

    Optics Letters
    |April 1, 2022
    PubMed
    Summary

    The diffractive deep neural network (D2NN) is a unitary transformation for optical fields, preserving inner products. This property makes D2NNs ideal for optical orthogonal mode applications, including mode conversion and recognition.

    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Diffractive deep neural networks (D2NNs) are emerging computational optical elements.
    • Understanding their fundamental properties is crucial for advanced optical applications.

    Purpose of the Study:

    • To uncover fundamental rules governing the behavior of optical fields within D2NNs.
    • To explore the suitability of D2NNs for optical orthogonal mode applications.

    Main Methods:

    • Theoretical analysis of D2NN properties, focusing on inner products and transformations.
    • Simulations to validate theoretical findings and demonstrate practical applications.

    Main Results:

    • The inner product of optical fields in a D2NN is invariant.

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  • D2NNs act as unitary transformations for optical fields.
  • Spatial separation of output intensities implies orthogonal input fields.

    • Conclusions:

    • D2NNs are well-suited for optical orthogonal mode applications such as mode conversion, multiplexing/demultiplexing, and optical mode recognition.
    • The discovered rules provide a theoretical foundation for designing D2NNs for specific optical tasks.