Deep Learning for Photonic Design and Analysis: Principles and Applications
Innovative techniques play important roles in photonic structure design and complex optical data analysis. As a branch of machine learning, deep learning can automatically reveal the inherent connections behind the data by using hierarchically structured layers, which has found broad applications in...
| Published in: | Frontiers in Materials |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-01-01
|
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2021.791296/full |
| _version_ | 1856990159838380032 |
|---|---|
| author | Bing Duan Bei Wu Jin-hui Chen Jin-hui Chen Huanyang Chen Da-Quan Yang |
| author_facet | Bing Duan Bei Wu Jin-hui Chen Jin-hui Chen Huanyang Chen Da-Quan Yang |
| author_sort | Bing Duan |
| collection | DOAJ |
| container_title | Frontiers in Materials |
| description | Innovative techniques play important roles in photonic structure design and complex optical data analysis. As a branch of machine learning, deep learning can automatically reveal the inherent connections behind the data by using hierarchically structured layers, which has found broad applications in photonics. In this paper, we review the recent advances of deep learning for the photonic structure design and optical data analysis, which is based on the two major learning paradigms of supervised learning and unsupervised learning. In addition, the optical neural networks with high parallelism and low energy consuming are also highlighted as novel computing architectures. The challenges and perspectives of this flourishing research field are discussed. |
| format | Article |
| id | doaj-art-e90cebb69efb4f76a4bca3a5b04f7250 |
| institution | Directory of Open Access Journals |
| issn | 2296-8016 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| spelling | doaj-art-e90cebb69efb4f76a4bca3a5b04f72502025-08-19T19:53:46ZengFrontiers Media S.A.Frontiers in Materials2296-80162022-01-01810.3389/fmats.2021.791296791296Deep Learning for Photonic Design and Analysis: Principles and ApplicationsBing Duan0Bei Wu1Jin-hui Chen2Jin-hui Chen3Huanyang Chen4Da-Quan Yang5State Key Laboratory of Information Photonics and Optical Communications, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaInstitute of Electromagnetics and Acoustics and Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, ChinaInstitute of Electromagnetics and Acoustics and Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, ChinaShenzhen Research Institute of Xiamen University, Shenzhen, ChinaInstitute of Electromagnetics and Acoustics and Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, ChinaState Key Laboratory of Information Photonics and Optical Communications, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaInnovative techniques play important roles in photonic structure design and complex optical data analysis. As a branch of machine learning, deep learning can automatically reveal the inherent connections behind the data by using hierarchically structured layers, which has found broad applications in photonics. In this paper, we review the recent advances of deep learning for the photonic structure design and optical data analysis, which is based on the two major learning paradigms of supervised learning and unsupervised learning. In addition, the optical neural networks with high parallelism and low energy consuming are also highlighted as novel computing architectures. The challenges and perspectives of this flourishing research field are discussed.https://www.frontiersin.org/articles/10.3389/fmats.2021.791296/fulloptics and photonicsdeep learningphotonic structure designoptical data analysisoptical neural networks |
| spellingShingle | Bing Duan Bei Wu Jin-hui Chen Jin-hui Chen Huanyang Chen Da-Quan Yang Deep Learning for Photonic Design and Analysis: Principles and Applications optics and photonics deep learning photonic structure design optical data analysis optical neural networks |
| title | Deep Learning for Photonic Design and Analysis: Principles and Applications |
| title_full | Deep Learning for Photonic Design and Analysis: Principles and Applications |
| title_fullStr | Deep Learning for Photonic Design and Analysis: Principles and Applications |
| title_full_unstemmed | Deep Learning for Photonic Design and Analysis: Principles and Applications |
| title_short | Deep Learning for Photonic Design and Analysis: Principles and Applications |
| title_sort | deep learning for photonic design and analysis principles and applications |
| topic | optics and photonics deep learning photonic structure design optical data analysis optical neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2021.791296/full |
| work_keys_str_mv | AT bingduan deeplearningforphotonicdesignandanalysisprinciplesandapplications AT beiwu deeplearningforphotonicdesignandanalysisprinciplesandapplications AT jinhuichen deeplearningforphotonicdesignandanalysisprinciplesandapplications AT jinhuichen deeplearningforphotonicdesignandanalysisprinciplesandapplications AT huanyangchen deeplearningforphotonicdesignandanalysisprinciplesandapplications AT daquanyang deeplearningforphotonicdesignandanalysisprinciplesandapplications |