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Deep learning-enhanced, open-source eigenmode expansion.

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    This summary is machine-generated.

    We developed EMEPy, an open-source Python package for eigenmode expansion (EME). This software uses artificial neural networks to speed up the EME process threefold, aiding educators and designers.

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    Area of Science:

    • Computational electromagnetics
    • Photonics simulation
    • Scientific software development

    Background:

    • Eigenmode expansion (EME) is a critical numerical method for simulating wave propagation in periodic structures.
    • Traditional EME methods can be computationally intensive, limiting design iteration speed.
    • Accelerating EME simulations is crucial for advancing photonic device design and analysis.

    Purpose of the Study:

    • To introduce EMEPy, an open-source software package for eigenmode expansion (EME) simulations.
    • To leverage artificial neural networks for accelerating the EME computational process.
    • To provide an accessible and user-friendly tool for researchers, educators, and new designers in photonics.

    Main Methods:

    • Developed EMEPy entirely in Python, ensuring broad compatibility and ease of use.
    • Integrated artificial neural networks to learn and reproduce electromagnetic eigenmode field profiles.
    • Implemented an intuitive scripting interface for seamless integration with other Python libraries.

    Main Results:

    • EMEPy accelerates the eigenmode expansion process by a factor of 3 compared to traditional methods.
    • The software accurately reproduces electromagnetic eigenmode field profiles using neural networks.
    • Demonstrated the package's utility through its intuitive scripting interface and compatibility.

    Conclusions:

    • EMEPy offers a significant speedup for EME simulations, enhancing design efficiency.
    • The use of artificial intelligence in EMEPy makes complex simulations more accessible.
    • EMEPy serves as a valuable educational tool and a practical resource for photonic designers.