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High-performance end-to-end deep learning IM/DD link using optics-informed neural networks.

Ioannis Roumpos, Lorenzo De Marinis, Manos Kirtas

|June 29, 2023

Summary

This summary is machine-generated.

Optics-informed neural networks improve deep learning for optical communication. Using a novel Photonic Sigmoid activation function, these models enhance performance in intensity modulation/direct detection fiber links.

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Area of Science:

Optical communications
Deep learning
Photonic device physics

Background:

End-to-end deep learning (DL) models are increasingly used for optical transmission systems.
Current models often utilize standard activation functions like ReLU, which may not fully leverage optical physics.
Neuromorphic photonics offers inspiration for novel DL components.

Purpose of the Study:

To introduce and experimentally validate optics-informed neural networks (NNs) for intensity modulation/direct detection (IM/DD) optical links.
To investigate the performance benefits of an optics-inspired activation function, the Photonic Sigmoid, in DL models.
To compare the proposed model against state-of-the-art ReLU-based configurations.

Main Methods:

Development of an optics-inspired NN architecture incorporating a Photonic Sigmoid activation function derived from semiconductor nonlinear optics.
Implementation of End-to-End DL configurations for fiber optic communication links.
Extensive simulations and experimental validation of the proposed model's performance.

Main Results:

Optics-informed NNs with the Photonic Sigmoid demonstrate superior noise and chromatic dispersion compensation compared to ReLU-based models.
The Photonic Sigmoid NNs achieve performance below the hard-decision forward error correction (HD FEC) limit.
Successful operation demonstrated for fiber lengths up to 42 km at 48 Gb/s.

Conclusions:

Optics-informed NNs represent a promising approach to enhance DL performance in optical communication systems.
The Photonic Sigmoid activation function offers significant advantages for IM/DD links, improving robustness and data rates.
This work bridges neuromorphic photonics and DL for practical advancements in fiber optic transmission.