Title | ||
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A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks. |
Abstract | ||
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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. |
Year | Venue | DocType |
---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.03387 | 0 | 0.34 |
References | Authors | |
0 | 14 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sensong An | 1 | 0 | 0.68 |
Clayton Fowler | 2 | 0 | 0.34 |
Bowen Zheng | 3 | 0 | 0.68 |
Mikhail Y. Shalaginov | 4 | 0 | 0.68 |
Hong Tang | 5 | 0 | 0.34 |
Hang Li | 6 | 0 | 0.34 |
li zhou | 7 | 58 | 10.92 |
Jun Ding | 8 | 6 | 3.58 |
Anuradha Murthy Agarwal | 9 | 0 | 0.34 |
Clara Rivero-Baleine | 10 | 0 | 0.34 |
Kathleen Richardson | 11 | 17 | 2.17 |
Tian Gu | 12 | 0 | 0.34 |
Juejun Hu | 13 | 0 | 1.69 |
Hualiang Zhang | 14 | 24 | 5.43 |