2020/11/30 by Peter R. Wiecha, Arnaud Arbouet, Christian Girard +1 · 475 citations
Computer Science · Engineering · Materials Science · Physics and Astronomy · #Artificial neural network #Context (archaeology) #Deep learning #Focus (optics) #Inverse #Inverse problem #Metamaterials and Metasurfaces Applications #Neural Networks and Reservoir Computing #Photonic and Optical Devices #cond-mat.mes-hall #physics.comp-ph #physics.optics
paper · pdf · doi:10.1364/prj.415960
published in Photonics Research 9(5), B182 (Optica Publishing Group) · Review article of 18 pages, 7 figures, 4 info-boxes
openalex created_date 2020/12/07 · arxiv created 2021/01/12 · openalex publication_date 2021/01/29 · arxiv updated 2021/09/03 · openalex updated_date 2026/08/05
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. 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.