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

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