A topological encoding method for data-driven photonics inverse design

Abstract

Data-driven approaches have been proposed as effective strategies for the inverse design and optimization of photonic structures in recent years. In order to assist data-driven methods for the design of topology of photonic devices, we propose a topological encoding method that transforms photonic structures represented by binary images to a continuous sparse representation. This sparse representation can be utilized for dimensionality reduction and dataset generation, enabling effective analysis and optimization of photonic topologies with data-driven approaches. As a proof of principle, we leverage our encoding method for the design of two dimensional non-paraxial diffractive optical elements with various diffraction intensity distributions. We proved that our encoding method is able to assist machine-learning-based inverse design approach for accurate and global optimization.

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Cited by in corpus (9)

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• Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems
• Data-Driven Design for Metamaterials and Multiscale Systems: A Review
• Active learning of deep surrogates for PDEs: Application to metasurface design
• t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
• Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
• Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials under Heterogeneous Fields
• Active Physics Informed Deep Learning: Surrogate Modeling for Non Planar Wavefront Excitation of Topological Nanophotonic Devices