期刊
NATURE COMMUNICATIONS
卷 9, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-018-07141-w
关键词
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资金
- Natural Sciences and Engineering Research Council of Canada (NSERC) through the Steacie, Strategic, Discovery and Acceleration Grants Schemes
- MESI PSR-SIIRI Initiative in Quebec
- Canada Research Chair Program
- Australian Research Council Discovery Projects scheme [DP100000104327]
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [725046]
- People Programme (Marie Curie Actions) of the European Union's FP7 Programme under REA grant agreement INCIPIT [PIOF-GA-2013-625466]
- European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska-Curie grant [656607]
- NSERC Vanier Canada Graduate Scholarships
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB24030300]
- Engineering and Physical Sciences Research Council (EPSRC) [EP/M013294/1]
- MC REA [630833, 327627]
- EU-H2020
- Government of the Russian Federation through the ITMO Fellowship and Professorship Program [074-U 01]
- 1000 Talents Sichuan Program
- EPSRC [1816379] Funding Source: UKRI
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.
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