Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 4884 |
| Fachzeitschrift | Nature Communications |
| Jahrgang | 9 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 20 Nov. 2018 |
| Extern publiziert | Ja |
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.
ASJC Scopus Sachgebiete
- Chemie (insg.)
- Allgemeine Chemie
- Biochemie, Genetik und Molekularbiologie (insg.)
- Allgemeine Biochemie, Genetik und Molekularbiologie
- Physik und Astronomie (insg.)
- Allgemeine Physik und Astronomie
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in: Nature Communications, Jahrgang 9, Nr. 1, 4884, 20.11.2018.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Customizing supercontinuum generation via on-chip adaptive temporal pulse-splitting
AU - Wetzel, Benjamin
AU - Kues, Michael
AU - Roztocki, Piotr
AU - Reimer, Christian
AU - Godin, Pierre Luc
AU - Rowley, Maxwell
AU - Little, Brent E.
AU - Chu, Sai T.
AU - Viktorov, Evgeny A.
AU - Moss, David J.
AU - Pasquazi, Alessia
AU - Peccianti, Marco
AU - Morandotti, Roberto
N1 - Publisher Copyright: © 2018, The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2018/11/20
Y1 - 2018/11/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056803445&partnerID=8YFLogxK
U2 - 10.1038/s41467-018-07141-w
DO - 10.1038/s41467-018-07141-w
M3 - Article
C2 - 30459363
AN - SCOPUS:85056803445
VL - 9
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 4884
ER -