Nonlinear Distortion Equalization in Multi-Span Optical Links Via a Feed-Forward Photonic Neural Network

TL;DR

This paper demonstrates a photonic neural network-based equalizer that effectively compensates for nonlinear and linear distortions in multi-span optical links, enabling high-speed optical signal processing with low latency.

Contribution

It introduces a novel integrated feed-forward photonic neural network for nonlinear distortion equalization in optical communications, validated through experiments and simulations.

Findings

Chromatic dispersion can be equalized over 200 km.

Self-phase modulation compensation up to 450 km.

Potential for 100 Gbps modulation adaptation.

Abstract

Linear and nonlinear distortions in optical communication signals are equalized using an integrated feed-forward Photonic Neural Network (PNN). The PNN is based on a linear stage made of an 8-tap Finite Impulse Response (FIR) filter, featuring tunable amplitude and phase weights at each tap, and of a nonlinear stage achieved through the square modulus operation at the end-of-line photodetector. Within an Intensity Modulation/Direct Detection (IMDD) system, the PNN is applied to 2-level Pulse Amplitude Modulated (PAM2) optical signals undergoing multi-span propagation. Each 50 km segment includes fiber transmission, optical power restoration, and optional chromatic dispersion compensation via a Tunable Dispersion Compensator. Positioned at the receiver, the PNN enables fully optical signal processing with minimal latency and power consumption. Experimental validation is conducted using a Silicon-On-Insulator device operating on 10 Gbps signals. It demonstrates chromatic dispersion equalization over distances up to 200 km and self-phase modulation (with dispersion removed) up to 450 km. Simulations explore PNN adaptation for 100 Gbps modulations and its potential for cross-phase modulation equalization.