Bayesian optimization of neural networks for the inverse design of all-dielectric metasurfaces

Eric S. Harper; Matthew S. Mills

doi:10.1117/12.2567754

20 August 2020 Bayesian optimization of neural networks for the inverse design of all-dielectric metasurfaces

Eric S. Harper, Matthew S. Mills

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 1146912 (2020) https://doi.org/10.1117/12.2567754
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

The next generation of multi-functional optical materials will customize electric field responses via a careful arrangement of micro- and nano- scale scatterers to achieve targeted optical performance otherwise unattainable in traditional bulk media. Macroscopically, such designed materials collectively respond to radiation based on the geometric shape, distribution, and inherent material properties of these sub-wavelength structures. The core challenge is in prescribing a configuration which results in a desired property. It becomes immediately clear these metamaterial systems pose significant challenges because of the near-infinite design space one needs to consider. Recently, artificial neural networks (ANNs) have been used to successfully approach this intractable problem. In the specific context of designing an all-dielectric metasurface reflector, we showed that joining two properly trained ANNs can both emulate Maxwell equations as well as inversely correlate reflection and transmission spectra.¹ Though highly accurate ANNs were trained, the ANNs employed were never optimized in terms of architecture (e.g. number of layers, number of nodes, shape of the network) or hyperparameters (e.g. batch sizes, activation functions, loss functions). In this manuscript, we apply Bayesian optimization with Gaussian processes to first optimize the architecture and then the hyperparameters of the spectra predicting networks described in Ref. 1. The goal is not only to improve upon the previously implemented ANNs but to analyze the effect of different ANN architectures and convergence settings on overall spectra predictive performance.

Conference Presentation

Citation Download Citation

Eric S. Harper and Matthew S. Mills "Bayesian optimization of neural networks for the inverse design of all-dielectric metasurfaces", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 1146912 (20 August 2020); https://doi.org/10.1117/12.2567754

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