Phase-controlled metasurface design via optimized genetic algorithm

Abstract

<jats:title>Abstract</jats:title><jats:p>In an optical Pancharatnam-Berry (PB) phase metasurface, each sub-wavelength dielectric structure of varied spatial orientation can be treated as a point source with the same amplitude yet varied relative phase. In this work, we introduce an optimized genetic algorithm (GA) method for the synthesis of one-dimensional (1D) PB phase-controlled dielectric metasurfaces by seeking for optimized phase profile solutions, which differs from previously reported amplitude-controlled GA method only applicable to generate transverse optical modes with plasmonic metasurfaces. The GA–optimized phase profiles can be readily used to construct dielectric metasurfaces with improved functionalities. The loop of phase-controlled GA consists of initialization, random mutation, screened evolution, and duplication. Here random mutation is realized by changing the phase of each unit cell, and this process should be efficient to obtain enough mutations to drive the whole GA process under supervision of appropriate mutation boundary. A well-chosen fitness function ensures the right direction of screened evolution, and the duplication process guarantees an equilibrated number of generated light patterns. Importantly, we optimize the GA loop by introducing a multi-step hierarchical mutation process to break local optimum limits. We demonstrate the validity of our optimized GA method by generating longitudinal optical modes (i. e., non-diffractive light sheets) with 1D PB phase dielectric metasurfaces having non-analytical counter-intuitive phase profiles. The produced large-area, long-distance light sheets could be used for realizing high-speed, low-noise light-sheet microscopy. Additionally, a simplified 3D light pattern generated by a 2D PB phase metasurface further reveals the potential of our optimized GA method for manipulating truly 3D light fields.</jats:p>

Experimental Demonstration of Genetic Algorithm Based Metalens Design for Generating Side‐Lobe‐Suppressed, Large Depth‐of‐Focus Light Sheet

“…[40][41][42][43][44][45][46] However, the latter usually requires much better computational hardware requirements due to the huge resource consumed by neural networks. By contrast, our previous theoretical work [7] has demonstrated the validity and versatility of our proposed GA based dielectric metalens design approach via studying the results of a 2D single plane and multiplane light sheets, all of which are free of dithering process.…”

“…cases, a genetic algorithm (GA) can be introduced to optimize the nanophotonics design when the physical relation between the input excitation and the output of optical field is known. [7,[29][30][31][32][33][34][35][36][37][38][39] In other cases, neural networks can be trained by examples to uncover the intricate and counter-intuitive relations between input variables and optical responses when their relationship is unknown before machine learning. [40][41][42][43][44][45][46] However, the latter usually requires much better computational hardware requirements due to the huge resource consumed by neural networks.…”

Intelligent designs in nanophotonics: from optimization towards inverse creation

“…The high numerical aperture (NA with a value of 1.48) meta-lens (oil immersion) exhibited a 207 nm full width at half maximum (FWHM) of a beam spot with an operational efficiency of 48%, representing one of highest NA of any metalens by that time. Other types of traditional algorithms such as GA and binary search techniques are also demonstrated in, e.g., Pancharatnam-Berry type metalens [76] as well as multi-level diffractive lenses [77][78][79], respectively.…”

Engineering Reflective Metasurfaces With Ising Hamiltonian and Quantum Annealing

“…Researchers have devoted substantial efforts in understanding how to devise pattern optimization methods to achieve a specific scattering profile, including genetic algorithms [18]- [20], impedance-based synthesis [9], electromagnetic inversion [21], statistical learning [22], as well as dynamical optimization via switching across profile states [23], [24]. Wavefront shaping has been demonstrated in the minimal case of binary reflection phase control, where a phase delay of either 0 or π can be impressed in the locally reflected field re-radiated by the metasurface element.…”