Deep Learning for The Inverse Design of Mid-infrared Graphene Plasmons

Autor:	Phan, Anh D., Nguyen, Cuong V., Linh, Pham T., Huynh, Tran V., Lam, Vu D., Le, Anh-Tuan
Rok vydání:	2019
Předmět:	Physics - Applied Physics Condensed Matter - Mesoscale and Nanoscale Physics Condensed Matter - Materials Science Physics - Optics
Druh dokumentu:	Working Paper
Popis:	We theoretically investigate the plasmonic properties of mid-infrared graphene-based metamaterials and apply deep learning of a neural network for the inverse design. These artificial structures have square periodic arrays of graphene plasmonic resonators deposited on dielectric thin films. Optical spectra vary significantly with changes in structural parameters. Our numerical results are in accordance with previous experiments. Then, the theoretical approach is employed to generate data for training and testing deep neural networks. By merging the pre-trained neural network with the inverse network, we implement calculations for inverse design of the graphene-based metameterials. We also discuss the limitation of the data-driven approach. Comment: 6 pages, 4 figures, accepted for publication on Crystals
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.12566 Zobrazit plný text záznamu View this record from Arxiv