Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency

Optics Express
Tian ZhangKun Xu

Abstract

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonics devices. We briefly review recent progress of this field of research and show data-driven applications, including spectrum prediction, inverse design and performance optimization, for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency (PIT) effect, which can be theoretically demonstrated by using the transfer matrix method. Some traditional machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, evolutionary algorithms, including single-objective (genetic algorithm) and multi-objective optimization (NSGA-II), are used to achieve the steep transmission characteristics of PIT effect by synthetically taking many different performance metrics into consideration. The maximum differ...Continue Reading

References

Mar 3, 2007·Nature Materials·A K Geim, K S Novoselov
Feb 21, 2008·Nano Letters·Alexander A BalandinChun Ning Lau
Sep 28, 2010·Physical Review Letters·Rohan D KekatpureMark L Brongersma
Sep 28, 2010·Physical Review Letters·E HendryS A Mikhailov
May 10, 2011·Nature·Ming LiuXiang Zhang
Jun 11, 2011·Science·Ashkan Vakil, Nader Engheta
Sep 6, 2011·Nature Nanotechnology·Long JuFeng Wang
Apr 20, 2012·ACS Nano·Qiaoliang Bao, Kian Ping Loh
Dec 25, 2012·Optics Express·Rasoul AlaeeFalk Lederer
Apr 3, 2013·Optics Express·Mohamed A K OthmanFilippo Capolino
Apr 4, 2013·Nano Letters·Hye-Young KimGeorg S Duesberg
Feb 12, 2014·Optics Express·Muhammad AminHakan Bağcı
Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jürgen Schmidhuber
Jul 15, 2015·Science·Daniel RodrigoHatice Altug
Jul 28, 2016·Optics Express·Louise F FrellsenLars H Frandsen
Aug 10, 2017·Optics Express·Yupeng ZhangLiming Yang
Jun 6, 2018·Science Advances·John PeurifoyMarin Soljačić
Sep 13, 2018·Nano Letters·Zhaocheng LiuWenshan Cai
Jan 16, 2019·Optics Express·Takashi Asano, Susumu Noda
Feb 6, 2019·Scientific Reports·Mohammad H TahersimaKieran Parsons
Mar 23, 2019·Science·Nasim Mohammadi EstakhriNader Engheta
May 31, 2019·Scientific Reports·Joshua BaxterLora Ramunno
Jul 31, 2019·Scientific Reports·Iman SajedianJunsuk Rho
Sep 10, 2019·Nanoscale·Zhao HuangJianfeng Zang
Nov 7, 2019·Optics Express·Alec M Hammond, Ryan M Camacho
Nov 7, 2019·Optics Express·Christian C NadellWillie J Padilla
Dec 7, 2019·Physical Review Letters·Yu LiZheyu Fang

❮ Previous
Next ❯

Related Concepts

Related Feeds

Cell Imaging in CNS

Here is the latest research on cell imaging and imaging modalities, including light-sheet microscopy, in the central nervous system.