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.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
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)

Creator
Ciarlo, Antonio
Ciriza, David Bronte
Selin, Martin
Maragò, Onofrio M.
Sasso, Antonio
Pesce, Giuseppe
Volpe, Giovanni
Goksör, Mattias

DOI
10.1515/nanoph-2024-0013
URN
urn:nbn:de:101:1-2407241753210.286548598425
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
14.08.2025, 10:50 AM CEST

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