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.

19 pages, 7 figures, 1 table

References in corpus (10)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Semi-Supervised Classification with Graph Convolutional Networks
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
• End-to-End Deep Reinforcement Learning for Lane Keeping Assist
• Deep learning and face recognition: the state of the art
• Speckle Optical Tweezers: Micromanipulation with Random Light Fields
• Learning to Denoise Astronomical Images with U-nets
• Variational autoencoder reconstruction of complex many-body physics
• Machine learning reveals complex behaviours in optically trapped particles
• A Classifying Variational Autoencoder with Application to Polyphonic Music Generation

Cited by in corpus (2)

• Faster calculations of optical trapping using neural networks trained by T-matrix data: an application to micro and nanoplastics
• Radiation forces and torques in optics and acoustics