Deep learning enabled inverse design in nanophotonics

Sunae So,Junsuk Rho,Jaebum Noh,Jorge Bravo-Abad,Trevon Badloe

doi:10.1515/nanoph-2019-0474

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.

Highlights

Nanophotonics is devoted to the study of light-matter interaction at the subwavelength scale [1]
We have summarized the recent progress of Deep learning (DL)-assisted inverse design in nanophotonics
As discussed in this work, DL provides a new platform for approximating Maxwell’s equations, and for the inverse design of various nanophotonic devices that can by far exceed human capability

Summary

Introduction

Nanophotonics is devoted to the study of light-matter interaction at the subwavelength scale [1]. During the last few decades, important fundamental advances combined with the spectacular progress of nanoscale fabrication methods [2,3,4] have led to a broad range of innovations in nanophotonics [5, 6], largely based on tailoring periodically structured materials to create 2D and 3D metasurfaces [7, 8] or metamaterials [9] that exhibit extraordinary properties that cannot be found in nature This includes advances in the fields of plasmonics [10, 11], holography [12, 13], artificial chirality [14, 15] and topological photonics [16, 17]. We end up with a set of conclusions of this work and outlook on the bright future of DL in nanophotonics

Deep learning for forward nanophotonic modelling

Supervised learning in inverse design

Unsupervised learning in inverse design

Reinforcement learning in inverse design

Findings

Conclusions and outlook