Multifunctional Metasurface Design with a Generative Adversarial Network (Advanced Optical Materials 5/2021)
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Metamaterials and Metasurfaces Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Computer Engineering University of Massachusetts Lowell Lowell MA 01854 USA
Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02319 USA
CREOL University of Central Florida Orlando FL 32816 USA
References
Reference metadata was not supplied for this record.