Ciarlo Antonio, Ciriza David Bronte, Selin Martin, Maragò Onofrio M, Sasso Antonio, Pesce Giuseppe, Volpe Giovanni, Goksör Mattias
Department of Physics, University of Gothenburg, Gothenburg, Sweden.
CNR-IPCF, Istituto per i Processi Chimico-Fisici, Messina, Italy.
Nanophotonics. 2024 May 23;13(17):3017-3035. doi: 10.1515/nanoph-2024-0013. eCollection 2024 Jul.
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.
光镊利用光与物质的相互作用来捕获从单个原子到微米大小的真核细胞等各种粒子。因此，光镊是物理学、生物学和纳米技术中一种普遍使用的工具。最近，深度学习的应用开始通过改进光镊的设计、校准和实时控制以及对捕获物体的跟踪和分析来增强光镊，由于深度学习具有更高的计算速度和通用性，其性能往往优于传统方法。从这个角度来看，我们展示了前沿的深度学习方法如何能显著改进光镊，并探索这种动态协同作用带来的令人兴奋的新的未来可能性。此外，我们还提供了关于以可靠且值得信赖的方式将深度学习与光镊和光操纵相结合的指导方针。
