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. Here, we review how deep learning has already remarkably improved optical tweezers, while exploring 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.
Cite
@article{arxiv.2401.02321,
title = {Deep Learning for Optical Tweezers},
author = {Antonio Ciarlo and David Bronte Ciriza and Martin Selin and Onofrio M. Maragò and Antonio Sasso and Giuseppe Pesce and Giovanni Volpe and Mattias Goksör},
journal= {arXiv preprint arXiv:2401.02321},
year = {2025}
}
Comments
19 pages, 7 figures, 1 table