Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 36,
  • Issue 18,
  • pp. 4066-4073
  • (2018)

Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine

Not Accessible

Your library or personal account may give you access

Abstract

In this paper, dispersion relations (DRs) of photonic crystals (PhCs) are computed by multilayer perceptron (MLP) and extreme learning machine (ELM) artificial neural networks (ANNs). Bi- and tri-dimensional optimized structures presenting distinct DRs and photonic band gaps (PBGs) were selected for case studies. Optical properties of a set of PhCs with similar geometries and different dimensions were calculated by an electromagnetic solver in order to provide input data for ANN training and testing. We demonstrate that simple- and fast-training ANN models are capable of providing accurate DRs’ curves in a very short time.

© 2018 IEEE

PDF Article
More Like This
Machine learning approach for computing optical properties of a photonic crystal fiber

Sunny Chugh, Aamir Gulistan, Souvik Ghosh, and B. M. A. Rahman
Opt. Express 27(25) 36414-36425 (2019)

Photonic extreme learning machine by free-space optical propagation

Davide Pierangeli, Giulia Marcucci, and Claudio Conti
Photon. Res. 9(8) 1446-1454 (2021)

Photonic extreme learning machine based on frequency multiplexing

Alessandro Lupo, Lorenz Butschek, and Serge Massar
Opt. Express 29(18) 28257-28276 (2021)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved