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Deep learning in nano-photonics: inverse design and beyond

Abstract : Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their respective applications and critically discuss their strengths and weaknesses. While a significant part of the community's attention lies on nano-photonic inverse design, deep learning has evolved as a tool for a large variety of applications. The second part of the review will focus therefore on machine learning research in nano-photonics "beyond inverse design". This spans from physics informed neural networks for tremendous acceleration of photonics simulations, over sparse data reconstruction, imaging and "knowledge discovery" to experimental applications.
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https://hal.laas.fr/hal-03040153
Contributor : Peter Wiecha <>
Submitted on : Friday, December 4, 2020 - 11:03:27 AM
Last modification on : Wednesday, June 9, 2021 - 10:00:25 AM

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Peter Wiecha, Arnaud Arbouet, Christian Girard, Otto L. Muskens. Deep learning in nano-photonics: inverse design and beyond. Photonics Research, OSA Publishing, 2021, 9 (5), pp. B182-B200. ⟨10.1364/PRJ.415960⟩. ⟨hal-03040153⟩

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