Reservoir computing is a neural-inspired approach for processing temporal data. In the optical domain, it exploits the complex dynamics of photonic systems for high-speed, energy-efficient computation. Various implementations, including semiconductor lasers and integrated photonics, enable applications in pattern recognition and signal processing.

The student's task is to provide an overview of optical reservoir computing, covering its principles, implementations, and applications.

Possible literature:

Duport, François, et al. "All-optical reservoir computing." Optics express 20.20 (2012): 22783-22795.

von Hünefeld, Guillermo, et al. "Enabling optical modulation format identification using an integrated photonic reservoir and a digital multiclass classifier." European Conference and Exhibition on Optical Communication. Optica Publishing Group, 2022.

Van der Sande, Guy, Daniel Brunner, and Miguel C. Soriano. "Advances in photonic reservoir computing." Nanophotonics 6.3 (2017): 561-576.

Voraussetzungen

The Gerchberg-Saxton algorithm is an iterative phase retrieval method originally developed in optics, and it has been adapted for electronic dispersion compensation in optical communication systems. The student will implement the algorithm and apply it to intensity-modulation/direct-detection (IM/DD) systems to explore its potential for waveform optimization. The focus lies on simulating the algorithm's behavior in the presence of fiber dispersion and analyzing convergence properties and performance.

Laser linewidth measurement is crucial for characterizing the spectral properties of lasers, particularly in applications requiring high precision. It involves determining the range of frequencies over which the laser emits coherent light. The student will perform experimental techniques to measure the linewidth. Understanding laser linewidth is essential for optimizing laser performance in communication, sensing, and metrology applications.