Machine learning for photonics: from computing to communication
Francesco Da Ros, Ali Cem, Yevhenii Osadchuk, Ognjen Jovanovic, Darko Zibar · 2023
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.