Genetic-algorithm-based deep neural networks for highly efficient photonic device design-Reference-Cited by-同舟云学术

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Genetic-algorithm-based deep neural networks for highly efficient photonic device design

Published:2021-05-24 Issue:6 Volume:9 Page:B247
ISSN:2327-9125
Container-title:Photonics Research
language:en
Short-container-title:Photon. Res.

Author:

Ren Yangming,Zhang Lingxuan,Wang Weiqiang^ORCID,Wang Xinyu,Lei Yufang,Xue Yulong,Sun Xiaochen,Zhang Wenfu

Funder

Strategic Priority Research Program of the Chinese Academy of Sciences

National Natural Science Foundation of China

Youth Innovation Promotion Association of Chinese Academy of Sciences

West Light Foundation of the Chinese Academy of Sciences

Natural Science Basic Research Program of Shaanxi

Research Project of Xiapos;an Postdoctoral Innovation Base

Publisher

The Optical Society

Subject

Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference34 articles.

1. Hybrid Silicon Photonics for Optical Interconnects

2. High-density and wide-bandwidth optical interconnects with silicon optical interposers [Invited]

3. Silicon and hybrid silicon photonic devices for intra-datacenter applications: state of the art and perspectives [Invited]

4. Large-scale nanophotonic phased array

5. Si Photonics for Practical LiDAR Solutions

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2. A 4×112 Gb/s Ultra-Compact Polarization-Insensitive Silicon Photonics WDM Receiver With CMOS TIA for Co-Packaged Optics and Optical I/O;Journal of Lightwave Technology;2024-09-01

3. Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data;Alexandria Engineering Journal;2024-06

4. Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks;The Open Bioinformatics Journal;2024-05-08

5. Predicting optical properties of different photonic crystal fibers from 2D structural images using convolutional neural network and transfer learning;Optics Communications;2024-05

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