Deep learning enabled inverse design in nanophotonics

By Sunae So, Trevon Badloe, Jae-Kyo Noh, Jorge Bravo-Abad, Junsuk Rho. Nanophotonics, 2020

473 citations recorded by the source corpus.

Abstract Deep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the inverse design of nanophotonic devices, mainly focusing on the three existing learning paradigms of supervised-, unsupervised-, and reinforcement learning. Deep learning forward modelling i.e. how artificial intelligence learns how to solve Maxwell’s equations, is also discussed, along with an outlook of this rapidly evolving research area.

Subfields: Atomic and Molecular Physics, and Optics, Biomedical Engineering, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/60df2f3598ea7d56ae24a649121907a6769eb444

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Steven G. Johnson
2. Lu Lu
3. Zhimin Shi
4. Mahmoud I. Hussein

In Biomedical Engineering

1. Zhicheng Wang
2. Lu Lu

In Electronic, Optical and Magnetic Materials

1. Zhicheng Wang
2. Lu Lu

Citations in the corpus

• Physics-informed neural networks with hard constraints for inverse design (2021, methodology): “Recently, deep learning in the form of deep neural networks has been used in many areas of inverse design, including nanophotonics [52, 17], mechanical materials [15], aerodynamics [49], and electromagnetism [47].”