A newcomer's guide to deep learning for inverse design in nano-photonics.

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices to achieve precise light-matter interactions using structural parameters and materials is a challenging task. Traditionally, solving this problem has relied on computationally expensive, iterative methods.

Evolutionary algorithms converge towards evolved biological photonic structures.

Nature features a plethora of extraordinary photonic architectures that have been optimized through natural evolution in order to more efficiently reflect, absorb or scatter light. While numerical optimization is increasingly and successfully used in photonics, it has yet to replicate any of these complex naturally occurring structures. Using evolutionary algorithms inspired by natural evolution and performing particular optimizations (maximize reflection for a given wavelength, for a broad range of wavelength or maximize the scattering of light), we have retrieved the most stereotypical natural photonic structures.