Automatic Inverse Design of High-Performance Beam-Steering Metasurfaces via Genetic-type Tree Optimization

We introduce a genetic-type tree search (GTTS) algorithm combined with unsupervised clustering for the automatic inverse design of high-performance metasurfaces. With the proposed method, we realize highly directive beam-steering metasurfaces via the cooptimization of the amplitude and phase. In comparison with previous topology optimization approaches, the developed GTTS algorithm optimizes the organization of subwavelength nanoantennas and, thus, is applicable to the design of both passive and active metasurfaces. The optimized beam-steering metasurface specifically exhibits a nearly constant directivity when the steering angle varies from 5 degrees to 30 degrees. Furthermore, the optimized nonintuitive reflectance and phase profiles assist in achieving highly directive beam steering when the phase modulation range is <18 degrees, which was previously challenging. Our approach can diminish the requirements of scattering light properties with substantially enhanced angular resolution of beam-steering metasurfaces, which enables the realization of high-performance metasurfaces that will be promising for a wide range of advanced nanophotonic applications.

Automated summary

The researchers have successfully achieved automatic inverse design of high-performance metasurfaces through a genetic-type tree search algorithm combined with unsupervised clustering, providing highly directive beam steering. The optimized metasurfaces exhibit consistent directivity at different beam steering angles, while also reducing the requirements for light scattering properties, offering potential for a wide range of advanced nanophotonic applications.