Authors:	G. von Huenefeld, B. Chacko, G. Ronniger, M. Kaveh, I. Sackey, M. Aghaeipour, P. Bienstman, C. Schubert, R. Freund
Title:	Neuromorphic reservoir for nonlinear optical signal equalization
Format:	International Conference Proceedings
Publication date:	1/2024
Journal/Conference/Book:	Photonics West
Editor/Publisher:	SPIE,
Volume(Issue):	p.12880-35
Location:	San Francisco, United States
Citations:	Look up on Google Scholar
Download:	(319KB)

Abstract

The upcoming optical telecommunication networks face a significant challenge due to a massive increase in internet traffic. To handle this, higher-capacity transmission schemes are being implemented. To increase the signal-to-noise ratios (OSNR), higher optical launch powers are used, which are limited by nonlinear distortions caused by the Kerr effect in the transmission fibers. Currently, expensive and power-hungry digital signal processing (DSP) solutions are used to tackle this problem. Our proposal offers an alternative solution using a neural network based on a photonic reservoir to address the nonlinear distortions in transmission links. This approach is potentially more cost-effective and consumes less energy. The photonic reservoir design is based on a four-port architecture incorporating multimode interferometers (MMIs), Mach-ZehnderInterferometers (MZIs), and semiconductor optical amplifiers (SOAs). Inside the reservoir, the optical signals from past and current transmissions are mixed, providing the network with a memory-like capability. The training process focuses solely on driving the MZI and SOA arrays, resulting in accurate outcomes with reduced training time and energy consumption. We numerically demonstrate the mitigation of nonlinearities on high-order transmission links using a photonic reservoir. By comparing various configurations of the neural network (NN), we highlight the specific advantages of each implementation. Looking ahead, we aim to implement this approach using a photonic integrated circuit (PIC) to further enhance its practicality and efficiency.