Nonlocal Interactions in Metasurfaces Harnessed by Neural Networks

Optical metasurfaces enable compact, lightweight and planar optical devices. Their performances, however, are still limited by design approximations imposed by their macroscopic dimensions. To address this problem, we propose a neural network-based multi-stage gradient optimization method to efficiently modulate nonlocal interactions between meta-atoms, which is one of the major effects neglected by current design methods. Our strategy allows for the use of these interactions as an additional design dimension to enhance the performance of metasurfaces and can be used to optimize large-scale metasurfaces with multiple parameters. As an example of application, we design a meta-hologram with a zero-order energy suppressed to 26% (theoretically) and 57% (experimentally) of its original value. Our results suggest that neural networks can be used as a powerful design tool for the next generation of high-performance metasurfaces with complex functionalities.