Multi-objective and categorical global optimization of photonic structures based on ResNet generative neural networks

Abstract

We show that deep generative neural networks, based on global topology optimization networks (GLOnets), can be configured to perform the multi-objective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin film stacks consisting of multiple material types. Benchmarks with known globally-optimized anti-reflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.

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Cited by in corpus (8)

• Deep neural networks for the evaluation and design of photonic devices
• Deep learning in nano-photonics: inverse design and beyond
• Global Inverse Design Across Multiple Photonic Structure Classes Using Generative Deep Learning
• Enhancing Adjoint Optimization-based Photonics Inverse Design with Explainable Machine Learning
• Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
• Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
• PyMoosh : a comprehensive numerical toolkit for computing the optical properties of multilayered structures
• PyMieDiff: A differentiable Mie scattering library