Customizing supercontinuum generation via on-chip adaptive temporal pulse-splitting

Benjamin Wetzel (), Michael Kues, Piotr Roztocki, Christian Reimer, Pierre-Luc Godin, Maxwell Rowley, Brent E. Little, Sai T. Chu, Evgeny A. Viktorov, David J. Moss, Alessia Pasquazi, Marco Peccianti and Roberto Morandotti ()
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Benjamin Wetzel: Université du Québec
Michael Kues: Université du Québec
Piotr Roztocki: Université du Québec
Christian Reimer: Université du Québec
Pierre-Luc Godin: Université du Québec
Maxwell Rowley: University of Sussex
Brent E. Little: Chinese Academy of Science
Sai T. Chu: City University of Hong Kong
Evgeny A. Viktorov: ITMO University
David J. Moss: Swinburne University of Technology
Alessia Pasquazi: University of Sussex
Marco Peccianti: University of Sussex
Roberto Morandotti: Université du Québec

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract Modern optical systems increasingly rely on complex physical processes that require accessible control to meet target performance characteristics. In particular, advanced light sources, sought for, for example, imaging and metrology, are based on nonlinear optical dynamics whose output properties must often finely match application requirements. However, in these systems, the availability of control parameters (e.g., the optical field shape, as well as propagation medium properties) and the means to adjust them in a versatile manner are usually limited. Moreover, numerically finding the optimal parameter set for such complex dynamics is typically computationally intractable. Here, we use an actively controlled photonic chip to prepare and manipulate patterns of femtosecond optical pulses that give access to an enhanced parameter space in the framework of supercontinuum generation. Taking advantage of machine learning concepts, we exploit this tunable access and experimentally demonstrate the customization of nonlinear interactions for tailoring supercontinuum properties.

Date: 2018
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DOI: 10.1038/s41467-018-07141-w

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