Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks.
Versatile neuromorphic photonic processor for next-generation optical communication systems
Building upon an 8-tap delayed silicon-on-insulator (SOI) photonic neural network (PNN) pre-equalizer validated at 10-GHz bandwidth orthogonal frequency-division multiplexing (OFDM) intensity-modulation direct detection (IMDD), we experimentally probe the operational boundaries of this architecture by scaling to 20-GHz bandwidth operation and 16-QAM modulation. We quantify performance across 5/10/20-GHz OFDM bandwidths and QAM4/QAM16, approaching the sampling limit imposed by the fixed 25-ps tap spacing. In this way, we experimentally demonstrate its applicability to preserve the signal integrity near the Nyquist limit with improvements in all tested configurations.