Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces

@article{An2021DeepCN,
  title={Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces},
  author={Sensong An and Bowen Zheng and Mikhail Y. Shalaginov and Hong Tang and Hang Li and Li Zhou and Yunxi Dong and Mohammad Haerinia and Anuradha Murthy Agarwal and Clara Rivero‐Baleine and Myungkoo Kang and Kathleen A. Richardson and Tian Gu and Juejun Hu and Clayton Fowler and Hualiang Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.01761}
}
Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since the nearfield coupling effects between elements will change when surrounded by non-identical structures. In this paper, we propose a deep learning approach to predict the actual electromagnetic (EM) responses of each target meta-atom… Expand
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