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

Abstract

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° to 30°. Furthermore, the optimized nonintuitive reflectance and phase profiles assist in achieving highly directive beam steering when the phase modulation range is <180°, 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.

Artificial Intelligence in Meta-optics

“…Copyright 2021 American Chemical Society. (c) Design diagram of a genetic-type tree search (GTTS) that integrates Monte Carlo tree search (MCTS) and a GA . Reprinted with permission from ref .…”

“…(c) Design diagram of a genetic-type tree search (GTTS) that integrates Monte Carlo tree search (MCTS) and a GA . Reprinted with permission from ref . Copyright 2021 American Chemical Society.…”

“…When the phase modulation range is less than 2π, or even less than π, the metasurface can still exhibit a strong beam -steering ability. As shown by the example in Figure c, Lin et al proposed a method named genetic-type tree search (GTTS), which originated from Monte Carlo tree search and a genetic algorithm with an unsupervised k -means clustering algorithm, to inversely design the high-performance beam-steering metasurface . They demonstrated three Al-based beam-steering metasurfaces for different wavelengths and a tunable beam-steering metasurface with Au–-graphene units.…”

Intelligent metaphotonics empowered by machine learning

“…Chiral metasurfaces are also among the typical applications of the DL design procedures [60,[84][85][86][87][88]. Phase-amplitude engineering supported by DL algorithms helps to overcome chromatism [89], achieve tunable beam-steering [62], and multiplex an aperture [90] of metasurfaces. AI-assisted design enables development and improvement of broadband achromatic [91,92], bifocal [58], thermally tunable [93], and RGB (red, green, blue) [94] metalenses.…”

Mechanically reprogrammable Pancharatnam–Berry metasurface for microwaves

“…To eliminate such amplitude-phase correlation-induced distortion in specific wavefront control, due consideration should be paid into inverse design optimization algorithms. 56,57 Moreover, each meta-atom in electrically or optically reprogrammable metasurfaces requires at least one PIN diode, photodiode, or varactor diode, [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] whose typical power dissipation is about several hundred milliwatts, and they need a continuous power supply to maintain the functionality. Such energy consumption entails a trade-off between the size/number of meta-atoms and the overall metasurface size, hindering large-scale applications.…”