Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > NOMA > 2021 > JW4B > JW4B.3

Neural-Network-based Design of Tunable Multilayer Films

Jungmin Kim, Sunkyu Yu, and Namkyoo Park

Not Accessible

Your library or personal account may give you access

Abstract

We introduce the data-driven design of multilayers with enhanced tunability. By applying the machine-learning-designed claddings to a phase-changeable core, we obtain the deterministic realization of on-off states in the angular transmittance, achieving tunable engineered disorder.

© 2021 The Author(s)

PDF Article
More Like This
Inverse design for integrated photonics using deep neural network

Keisuke Kojima, Toshiaki Koike-Akino, Yingheng Tang, and Ye Wang
IF3A.6 Integrated Photonics Research, Silicon and Nanophotonics (IPR) 2021

Dimensionality Reduction Based Method for Design and Optimization of Optical Nanostructures Using Neural Network

Mohammadreza Zandehshahvar, Omid Hemmatyar, Yashar Kiarashinejad, Sajjad Abdollahramezani, and Ali Adibi
FM5C.2 Frontiers in Optics (FiO) 2019

Agile dispersion engineering of photonic waveguides by using neural network aided inverse design

Zhaonian Wang, Jiangbing Du, Weihong Shen, Jiacheng Liu, Ke Xu, and Zuyuan He
W4F.5 Optoelectronics and Communications Conference (OECC) 2021

View More...
Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use