Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (elektronisch) | 9781728104690 |
| Publikationsstatus | Veröffentlicht - 1 Juni 2019 |
| Extern publiziert | Ja |
| Veranstaltung | 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019 - Munich, Deutschland Dauer: 23 Juni 2019 → 27 Juni 2019 |
Abstract
Modern optical systems increasingly rely on complex physical processes. Advanced light sources, such as supercontinuum (SC) [1], are highly sought for imaging and metrology, and are based on nonlinear dynamics where the output properties must often finely match target performance characteristics. However, in these systems, the availability of control parameters and the means to adjust them in a versatile manner are usually limited. Moreover, finding the ideal parameters for a specific application can become inherently complex. Here, we use an actively-controlled photonic chip to prepare and manipulate patterns of femtosecond optical pulses seeding supercontinuum generation [1]. Taking advantage of machine learning concepts [2], we exploit this access to an enhanced and tunable parameter space and experimentally demonstrate the customization of nonlinear interactions responsible for tailoring supercontinuum properties [3].
ASJC Scopus Sachgebiete
- Chemie (insg.)
- Spektroskopie
- Werkstoffwissenschaften (insg.)
- Elektronische, optische und magnetische Materialien
- Physik und Astronomie (insg.)
- Instrumentierung
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
- Informatik (insg.)
- Computernetzwerke und -kommunikation
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8872354.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Customizing supercontinuum generation via adaptive on-chip 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: © 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Modern optical systems increasingly rely on complex physical processes. Advanced light sources, such as supercontinuum (SC) [1], are highly sought for imaging and metrology, and are based on nonlinear dynamics where the output properties must often finely match target performance characteristics. However, in these systems, the availability of control parameters and the means to adjust them in a versatile manner are usually limited. Moreover, finding the ideal parameters for a specific application can become inherently complex. Here, we use an actively-controlled photonic chip to prepare and manipulate patterns of femtosecond optical pulses seeding supercontinuum generation [1]. Taking advantage of machine learning concepts [2], we exploit this access to an enhanced and tunable parameter space and experimentally demonstrate the customization of nonlinear interactions responsible for tailoring supercontinuum properties [3].
AB - Modern optical systems increasingly rely on complex physical processes. Advanced light sources, such as supercontinuum (SC) [1], are highly sought for imaging and metrology, and are based on nonlinear dynamics where the output properties must often finely match target performance characteristics. However, in these systems, the availability of control parameters and the means to adjust them in a versatile manner are usually limited. Moreover, finding the ideal parameters for a specific application can become inherently complex. Here, we use an actively-controlled photonic chip to prepare and manipulate patterns of femtosecond optical pulses seeding supercontinuum generation [1]. Taking advantage of machine learning concepts [2], we exploit this access to an enhanced and tunable parameter space and experimentally demonstrate the customization of nonlinear interactions responsible for tailoring supercontinuum properties [3].
UR - http://www.scopus.com/inward/record.url?scp=85074629273&partnerID=8YFLogxK
U2 - 10.1109/cleoe-eqec.2019.8872354
DO - 10.1109/cleoe-eqec.2019.8872354
M3 - Conference contribution
AN - SCOPUS:85074629273
BT - 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019
Y2 - 23 June 2019 through 27 June 2019
ER -