Machine learning for photonics: from computing to communication

被引：0

作者：

Da Ros, Francesco ^{[1
]}

Cem, Ali ^{[1
]}

Osadchuk, Yevhenii ^{[1
]}

Jovanovic, Ognjen ^{[1
]}

Zibar, Darko ^{[1
]}

机构：

[1] Tech Univ Denmark, Lyngby, Denmark

来源：

2023 IEEE PHOTONICS SOCIETY SUMMER TOPICALS MEETING SERIES, SUM | 2023年

关键词：

NN models; matrix multipliers; equalization;

D O I：

10.1109/SUM57928.2023.10224400

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Neural networks are effective tools for learning direct and inverse models. Here, we review two specific applications of neural networks to photonics: (i) learning accurate direct models for optical matrix multipliers and (ii) inverse modeling for short-reach fiber communication systems, enabling signal equalization.

引用

页数：2

共 50 条

[21] Computing with Categories in Machine Learning
Sennesh, Eli
Xu, Tom
Maruyama, Yoshihiro
ARTIFICIAL GENERAL INTELLIGENCE, AGI 2023, 2023, 13921 : 244 - 254
[22] Machine Learning as Granular Computing
Hu, Hong
Shi, Zhongzhi
2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009), 2009, : 229 - 234
[23] Granular computing in machine learning
Degang Chen
Weihua Xu
Jinhai Li
Granular Computing, 2019, 4 : 299 - 300
[24] Granular computing in machine learning
Chen, Degang
Xu, Weihua
Li, Jinhai
GRANULAR COMPUTING, 2019, 4 (03) : 299 - 300
[25] Integrated Phase-change Photonics: A Strategy for Merging Communication and Computing
Wright, C. David
Bhaskaran, Harish
Pernice, Wolfram. H. P.
Carrillo, Santiago G-C
Gemo, Emanuele
Baldycheva, Anna
Cheng, Zengguang
Li, Xuan
Rios, Carlos
Youngblood, Nathan
Feldmann, Johannes
Gruhler, Nico
Stegmaier, Matthias
2019 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC), 2019,
[26] Future prospects of silicon photonics in next generation communication and computing systems
Yoo, S. J. B.
ELECTRONICS LETTERS, 2009, 45 (12) : 584 - 587
[27] Machine learning to optimize additive manufacturing for visible photonics
Lininger, Andrew
Aththanayake, Akeshi
Boyd, Jonathan
Ali, Omar
Goel, Madhav
Jizhe, Yangheng
Hinczewski, Michael
Strangi, Giuseppe
NANOPHOTONICS, 2023, 12 (14) : 2767 - 2778
[28] Advances in machine learning optimization for classical and quantum photonics
Sanchez, M.
Everly, C.
Postigo, P. A.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS, 2024, 41 (02) : A177 - A190
[29] Machine Learning Application For Silicon Photonics Transceiver Testing
Kim, Woosung
Seng, Yeoh Hoe
Chang, Yi-Shing
Mei, Suohai
OPTICS AND PHOTONICS FOR INFORMATION PROCESSING XII, 2018, 10751
[30] MACHINE LEARNING IN COMMUNICATION NETS
LEHMANN, F
SEISING, R
WALTHERKLAUS, E
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, 1992, 604 : 304 - 313

← 1 2 3 4 5 →