Suppressing the sample-to-sample variation of photonic crystal nanocavity Q-factors by air-hole patterns with broken mirror symmetry

Artificial neural network assisted the design of subwavelength-grating waveguides for nanoparticles optical trapping

In this work, we propose artificial neural networks (ANNs) to predict the optical forces on particles with a radius of 50 nm and inverse-design the subwavelength-grating (SWG) waveguides structure for trapping. The SWG waveguides are applied to particle trapping due to their superior bulk sensitivity and surface sensitivity, as well as longer working distance than conventional nanophotonic waveguides. To reduce the time consumption of the design, we train ANNs to predict the trapping forces and to inverse-design the geometric structure of SWG waveguides, and the low mean square errors (MSE) of the networks achieve 2.8 × 10-4. Based on the well-trained forward prediction and inverse-design network, an SWG waveguide with significant trapping performance is designed. The trapping forces in the y-direction achieve-40.39 pN when the center of the particle is placed 100 nm away from the side wall of the silicon segment, and the negative sign of the optical forces indicates the direction of the forces. The maximum trapping potential achieved to 838.16 kBT in the y-direction. The trapping performance in the x and z directions is also quite superior, and the neural network model has been further applied to design SWGs with a high trapping performance. The present work is of significance for further research on the application of artificial neural networks in other optical devices designed for particle trapping.