Fig. 7: Inversely designed optical metasurfaces with deep learning.

a A tandem strategy involves two neural networks, forward neural networks and inverse neural networks¹²⁰. a.i Schematic of data preparation for the deep learning model. a.ii Examples of the inverse design of new supercell metasurfaces exhibiting triple-narrowband (top) and graybody behaviors (bottom). b Schematic of the inverse design framework implementing the generative model with the adjoint-optimization method (DeepAdjoint)¹²⁵. c Deep Q-networks model for nanophotonics¹³⁰. c.i Schematic of a one-dimensional Si metagrating structure that splits into N = 64 cells with +1 or −1 representing Si and air, respectively. c.ii Optimization results of the Si metagrating beam deflector. Deflection efficiencies during the overall training with the deep Q network (green) and random search (gray) with the darker line indicating the mean value (left) and electric field distribution of the optimized device (right). d Physics-informed neural networks (PINN) model for large-area metalens³⁶. d.i Optimization framework of two dimensional meta-optics using PINNs. d.ii Comparison of intensity profiles from lenses designed using local phase approximation and PINNs. d.iii Efficiencies of both lenses versus the numerical aperture.