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

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 two‐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.