Multifunctional Metasurface Design with a Generative Adversarial Network (Advanced Optical Materials 5/2021)

This cover picture illustrates the deep‐learning approach for designing multifunctional all‐dielectric meta‐devices as proposed by Clayton Fowler, Hualiang Zhang, and co‐workers in article number 2001433. With the help of a fully‐trained Generative Adversarial Network (GAN), various meta‐atom design...

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Bibliographic Details
Published in:Advanced optical materials 2021-03, Vol.9 (5), p.n/a
Main Authors: An, Sensong, Zheng, Bowen, Tang, Hong, Shalaginov, Mikhail Y., Zhou, Li, Li, Hang, Kang, Myungkoo, Richardson, Kathleen A., Gu, Tian, Hu, Juejun, Fowler, Clayton, Zhang, Hualiang
Format: Article
Language:English
Subjects:
deep learning
inverse design
metasurfaces
neural networks
photonics
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Summary:This cover picture illustrates the deep‐learning approach for designing multifunctional all‐dielectric meta‐devices as proposed by Clayton Fowler, Hualiang Zhang, and co‐workers in article number 2001433. With the help of a fully‐trained Generative Adversarial Network (GAN), various meta‐atom designs can be generated based on different design goals, which can be used to achieve multifunctional meta‐optics devices/systems (e.g. frequency multiplexed metalens as shown in the cover image). Several metasurface devices including multifunctional beam deflectors and lenses are designed and verified to showcase the efficacy of the proposed deep‐learning framework. This approach offers new opportunities to design and advance meta‐optic devices with enhanced time efficiency and performance.
ISSN:2195-1071
2195-1071
DOI:10.1002/adom.202170019