Journal Article Review Epublish 5 citations
PubMed: 39634937 DOI: 10.1515/nanoph-2024-0013
Deep learning for optical tweezers.
Nanophotonics 13(17):3017-3035
Authors: Antonio Ciarlo, David Bronte Ciriza, Martin Selin, Onofrio M Maragò, Antonio Sasso, Giuseppe Pesce, Giovanni Volpe, Mattias Goksör
Affiliations
- Department of Physics, University of Gothenburg, Gothenburg, Sweden.
- CNR-IPCF, Istituto per i Processi Chimico-Fisici, Messina, Italy.
- Dipartimento di Fisica "Ettore Pancini", Università degli Studi di Napoli Federico II, Naples, Italy.
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.
MeSH
Deep Learning, Learning