Deep learning enabled inverse design in nanophotonics

TLDR

Deep learning, originally dominant in computer science, has expanded to physics, chemistry, and materials, and is now used in nanophotonics to map the nonlinear relationship between nanostructure topology/composition and functional properties. The paper reviews how AI learns to solve Maxwell’s equations and surveys progress in inverse design of nanophotonic devices using supervised, unsupervised, and reinforcement learning. Recent work applying deep learning to inverse design of nanophotonic devices has been examined, emphasizing supervised, unsupervised, and reinforcement learning paradigms. Deep learning forward modelling is discussed.

Abstract

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.