Deep learning the design of optical components

Joeri Lenaerts; Hannah Pinson; Vincent Ginis

doi:10.1117/12.2568664

20 August 2020 Deep learning the design of optical components

Joeri Lenaerts, Hannah Pinson, Vincent Ginis

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 1146909 (2020) https://doi.org/10.1117/12.2568664
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

In addition to the celebrated numerical techniques, such as Finite-Element and Finite-Difference methods, it is also possible to predict the scattering properties of optical components using artificial neural networks. However, these machine-learning models typically suffer from a simplicity versus accuracy trade-off. In our work, we overcome this trade-off. We train several neural networks with an indirect goal. Instead of training the net to predict scattering, we try to train it the laws of Optics on a more fundamental level. In this way, we can increase the predictive power and robustness while maintaining a high degree of transparency in the system.

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Joeri Lenaerts, Hannah Pinson, and Vincent Ginis "Deep learning the design of optical components", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 1146909 (20 August 2020); https://doi.org/10.1117/12.2568664

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KEYWORDS

Optical components

Optical design

Neural networks

Scattering

Artificial neural networks

Finite difference methods

Light scattering

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