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Deep Neural Networks for Inverse Design of Nanophotonic Devices

Deep learning is now playing a major role in designing photonic devices, including nanostructured photonics. In this article, we investigate three models for designing nanophonic power splitters with multiple splitting ratios. The first model is a forward regression model, wherein the trained deep neural network (DNN) is used within the optimization loop. The second is an inverse regression model, in which the trained DNN constructs a structure with the desired target performance given as input. The third model is a generative network, which can randomly produce a series of optimized designs for a target performance. Focusing on the nanophotonic power splitters, we show how the devices can be designed by these three types of DNN models.

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Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.4 February 15 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1010 - 1019

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

NEURAL NETWORKS
NANOPHOTONICS
BENTUK GALAKSI
INTEGRATED PHOTONICS
INVERSE DESIGN
GENERATIVE NEURAL NETWORKS

Info Detail Spesifik

DOI: 10.1109/JLT.2021.3050083

Pernyataan Tanggungjawab

Keisuke Kojima, Mohammad H. Tahersima, Toshiaki Koike-Akino, Devesh K. Jha, Yingheng Tang, Ye Wang, Kieran Parsons

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