Deep learning for optical tweezers
Abstract: Optical tweezers exploit light–matter interactions to trap particles ranging from single atoms to micrometer-sized eukaryotic cells. For this reason, optical tweezers are a ubiquitous tool in physics, biology, and nanotechnology. Recently, the use of deep learning has started to enhance optical tweezers by improving their design, calibration, and real-time control as well as the tracking and analysis of the trapped objects, often outperforming classical methods thanks to the higher computational speed and versatility of deep learning. In this perspective, we show how cutting-edge deep learning approaches can remarkably improve optical tweezers, and explore the exciting, new future possibilities enabled by this dynamic synergy. Furthermore, we offer guidelines on integrating deep learning with optical trapping and optical manipulation in a reliable and trustworthy way.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Deep learning for optical tweezers ; volume:13 ; number:17 ; year:2024 ; pages:3017-3035 ; extent:19
Nanophotonics ; 13, Heft 17 (2024), 3017-3035 (gesamt 19)
- Urheber
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Ciarlo, Antonio
Ciriza, David Bronte
Selin, Martin
Maragò, Onofrio M.
Sasso, Antonio
Pesce, Giuseppe
Volpe, Giovanni
Goksör, Mattias
- DOI
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10.1515/nanoph-2024-0013
- URN
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urn:nbn:de:101:1-2407241753210.286548598425
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:50 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Ciarlo, Antonio
- Ciriza, David Bronte
- Selin, Martin
- Maragò, Onofrio M.
- Sasso, Antonio
- Pesce, Giuseppe
- Volpe, Giovanni
- Goksör, Mattias
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