Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Finding the Right Deep Neural Network Model for Efficient Design of Tunable Nanophotonic Devices

Not Accessible

Your library or personal account may give you access

Abstract

We develop generative deep neural networks that explore relevant statistical structures to expedite a complex inverse design of nanophotonic on-chip wavelength de-multiplexer. Our design, targeting at telecomm-wavelengths, is electrically switchable via liquid crystal tuning.

© 2022 The Author(s)

PDF Article  |   Presentation Video
More Like This
Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design

Toshiaki Koike-Akino, Minwoo Jung, Ankush Chakrabarty, Ye Wang, Keisuke Kojima, and Matthew Brand
FW4C.7 CLEO: Fundamental Science (CLEO:FS) 2023

AutoML Hyperparameter Tuning of Generative DNN Architecture for Nanophotonic Device Design

Toshiaki Koike-Akino, Keisuke Kojima, and Ye Wang
JW3A.44 CLEO: Applications and Technology (CLEO:A&T) 2022

Deep Transfer Learning for Nanophotonic Device Design

Keisuke Kojima, Minwoo Jung, Toshiaki Koike-Akino, Ye Wang, Matthew Brand, and Kieran Parsons
CFA12E_05 Conference on Lasers and Electro-Optics/Pacific Rim (CLEO/PR) 2022

Presentation Video

Presentation video access is available to:

  1. Optica Publishing Group subscribers
  2. Technical meeting attendees
  3. Optica members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access Optica Member Subscription or free downloads


More Like This
Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design

Toshiaki Koike-Akino, Minwoo Jung, Ankush Chakrabarty, Ye Wang, Keisuke Kojima, and Matthew Brand
FW4C.7 CLEO: Fundamental Science (CLEO:FS) 2023

AutoML Hyperparameter Tuning of Generative DNN Architecture for Nanophotonic Device Design

Toshiaki Koike-Akino, Keisuke Kojima, and Ye Wang
JW3A.44 CLEO: Applications and Technology (CLEO:A&T) 2022

Deep Transfer Learning for Nanophotonic Device Design

Keisuke Kojima, Minwoo Jung, Toshiaki Koike-Akino, Ye Wang, Matthew Brand, and Kieran Parsons
CFA12E_05 Conference on Lasers and Electro-Optics/Pacific Rim (CLEO/PR) 2022

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

Inverse Design of Nanophotonic Devices using Deep Neural Networks

Keisuke Kojima, Yingheng Tang, Toshiaki Koike-Akino, Ye Wang, Devesh Jha, Kieran Parsons, Mohammad H. Tahersima, Fengqiao Sang, Jonathan Klamkin, and Minghao Qi
Su1A.1 Asia Communications and Photonics Conference (ACP) 2020

Select as filters


Select Topics Cancel
© Copyright 2023 | Optica Publishing Group. All Rights Reserved