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

Deep Neural Network Inverse Modeling for Integrated Photonics

Not Accessible

Your library or personal account may give you access

Abstract

We propose a deep neural network model that instantaneously predicts the optical response of nanopatterned silicon photonic power splitter topologies, and inversely approximates compact (2.6×2.6 µm2) and efficient (above 92%) power splitters for target splitting ratios.

© 2019 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 (IPRSN) 2021

Deep Neural Networks for Designing Integrated Photonics

Keisuke Kojima, Mohammad H. Tahersima, Toshiaki Koike-Akino, Devesh Jha, Yingheng Tang, Ye Wang, Kieran Parsons, Fengqiao Sang, and Jonathan Klamkin
Th1A.6 Optical Fiber Communication Conference (OFC) 2020

Nanostructured Photonic Power Splitter Design via Convolutional Neural Networks

Mohammad H. Tahersima, Keisuke Kojima, Toshiaki Koike-Akino, Devesh Jha, Bingnan Wang, Chungwei Lin, and Kieran Parsons
SW4J.6 CLEO: Science and Innovations (CLEO_SI) 2019

References

You do not have subscription access to this journal. Citation lists with outbound citation 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 2022 | Optica Publishing Group. All Rights Reserved