A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks
This addresses the design bottleneck for optical engineers working on flat optical devices, offering a generic solution that overcomes previous neural network limitations.
The paper tackles the slow and inaccurate trial-and-error design of metasurfaces by introducing a deep neural network approach that improves speed and accuracy for generating meta-atom structures and devices across the electromagnetic spectrum.
Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.