Inverse design of optical cages by using a fully connected neural network and time-reversed dipole radiation

Abstract

In the conventional scheme, the focal field is designed by selecting appropriate dipole parameters based on theory of the time-revered dipole radiation. The time-revered method relies on empirical judgment and iterative parameter testing to create only a few special optical cages, involving vast time-consuming global parameter scanning and prescreening. In this work, a combination of a time-reversed dipole radiation model and a fully connected neural networks (FCNN) is introduced as the inverse design of optical cages. Flexibility and little time consumption in the design of the light cage are the two pivotal advantages. The dual light needle focusing fields with varying lengths and the synthesis of light cages featuring bubble focusing fields with distinct characteristics are attained, which are prescribed focal structures with freedom. With the help of the FCNN, the required dipole parameters for generating the expected focal fields are obtained quickly. This approach opens up possibilities for the utilization of neural networks in the design and modulation of more structured focusing fields. The achieved optical cage holds great potential for diverse applications, such as multiparticle trapping, manipulation, assembly, and enhanced nanoscale optical operations.