期刊
ACS PHOTONICS
卷 6, 期 12, 页码 3196-3207出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsphotonics.9b00966
关键词
deep learning; deep neural network; all-dielectric metasurface; objective-driven design; inverse design
类别
资金
- Defense Advanced Research Projects Agency Defense Sciences Office (DSO) Program: EXTREME Optics and Imaging (EXTREME) [HR00111720029]
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) responses, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep learning modeling approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to characterize the subwavelength optical structures. Our neural network approach overcomes two key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch and accurate EM-wave phase prediction. Additionally, this is the first neural network to characterize 3-D dielectric structures. By combining with optimization algorithms or neural networks, this approach 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.
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