Driven semiconductor lasers for information processing

Mirko Goldmann; Apostolos Argyris; Ingo Fischer; Miguel C. Soriano

doi:10.1117/12.3022136

18 June 2024 Driven semiconductor lasers for information processing

Mirko Goldmann, Apostolos Argyris, Ingo Fischer, Miguel C. Soriano

Proceedings Volume PC13017, Machine Learning in Photonics; PC130170T (2024) https://doi.org/10.1117/12.3022136
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Photonic systems, exhibiting multi-gigahertz bandwidth, facilitate data transmission at gigabit-per-second rates. While traditionally used in optical communication for data transfer, semiconductor lasers are now being explored for their potential in optical computation and signal processing. Injecting information into these lasers leads to nonlinear transformations and high-speed processing. Experimentally, a single semiconductor laser shows essential features for versatile computation, such as high-dimensional and nonlinear responses within sub-nanoseconds. To boost computational power, we study numerically the training of delay-coupled laser networks. The objective is, akin to training artificial neural networks, optimizing laser network's to improve performance and computational efficiency in challenging machine learning tasks. However, relying on offline optimization methods and physical models raises challenges due to device variability and limited system observability. Here, we propose evolutionary strategies to optimize physical systems without needing precise model knowledge, offering a promising approach for online system optimization.

Conference Presentation

Citation Download Citation

Mirko Goldmann, Apostolos Argyris, Ingo Fischer, and Miguel C. Soriano "Driven semiconductor lasers for information processing", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170T (18 June 2024); https://doi.org/10.1117/12.3022136

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