A silicon photonics feed-forward neural network for nonlinear distortion mitigation in an optical link

TL;DR

This paper presents a passive silicon photonics neural network designed to mitigate nonlinear distortions in optical links, using microring resonators and particle swarm optimization for training.

Contribution

It introduces a novel all-optical neural network architecture with tunable microring resonators for nonlinear distortion compensation in optical communications.

Findings

Effective mitigation of linear and nonlinear distortions.

Broad range of signal-to-noise ratios and propagation lengths.

Single-layer, single-node perceptron achieves significant correction.

Abstract

We design and model a single-layer, passive, all-optical silicon photonics neural network to mitigate optical link nonlinearities. The network nodes are formed by silicon microring resonators whose transfer function has been experimentally measured. Both the transmitted amplitude and phase maps of the nonlinear response of the microrings are parametrized as a function of the wavelength and of the signal power to form tunable activation functions of the single nodes in the complex valued network. Training of the network is achieved by a particle swarm optimizer which selects the complex weights and the activation functions. We demonstrate that a single feed-forward layer with a single node perceptron is effective in compensating linear and nonlinear distortions over a broad range of signal-to-noise-ratio and propagation lengths. We propose to implement this simple neuronal network as an optical link transparent layer to correct signal distortions.