Free-form optimization of nanophotonic devices: from classical methods to deep learning

Nanophotonic devices have enabled microscopic control of light with an unprecedented spatial resolution by employing subwavelength optical elements that can strongly interact with incident waves. However, to date, most nanophotonic devices have been designed based on fixed-shape optical elements, and a large portion of their design potential has remained unexplored. It is only recently that free-form design schemes have been spotlighted in nanophotonics, offering routes to make a break from conventional design constraints and utilize the full design potential. In this review, we systematically overview the nascent yet rapidly growing field of free-form nanophotonic device design. We attempt to define the term free-form in the context of photonic device design, and survey different strategies for free-form optimization of nanophotonic devices spanning from classical methods, adjoint-based methods, to contemporary machine-learning-based approaches.

Automated summary

This review systematically overviews the nascent yet rapidly growing field of free-form nanophotonic device design. Different strategies for free-form optimization of nanophotonic devices are surveyed, including classical methods, adjoint-based methods, and contemporary machine-learning-based approaches.