Efficient optical trapping force tuning for cusp-catastrophe autofocusing beams using deep neural networks

Abstract

Structured light adjusts optical trapping forces through flexible structure design. However, it is challenging to evaluate optical forces on microscopic particles in structured light due to high computational hardware requirements, prolonged computation times, and data inefficiencies associated with solving optical trapping forces using generalized Lorenz–Mie theory. We propose the use of deep neural networks for predicting and tuning the optical trapping force of cusp-catastrophe autofocusing beams on Mie particles. Inputs include beam's structural parameters, laser power, and the size of captured particle, while the output is the optical trapping force. Following iterative training, the neural network achieved a mean square error of 1.5×10−5. Evaluation using 150 sets of test data revealed that 95.3% of the predictions had a relative error of less than 1.8%, indicating a high prediction accuracy. In contrast to traditional computational methods, the neural network model demonstrates a remarkable efficiency improvement—104 times faster in optimizing beams for optical trapping. This advancement demonstrates the advantage of deep learning neural networks for the application of structured light including autofocusing beams in optical tweezers.