Array-Level Inverse Design of Beam Steering Active Metasurfaces

Abstract

We report an array-level inverse design approach to optimize the beam steering performance of active metasurfaces, thus overcoming the limitations posed by nonideal metasurface phase and amplitude tuning. In contrast to device-level topology optimization of passive metasurfaces, the outlined system-level optimization framework relies on the electrical tunability of geometrically identical nanoantennas, enabling the design of active antenna arrays with variable spatial phase and amplitude profiles. Based on this method, we demonstrate high-directivity, continuous beam steering up to 70° for phased arrays with realistic tunable antenna designs, despite nonidealities such as strong covariation of scattered light amplitude with phase. Nonintuitive array phase and amplitude profiles further facilitate beam steering with a phase modulation range as low as 180°. Furthermore, we use the device geometries presented in this work for experimental validation of the system-level inverse design approach of active beam steering metasurfaces. The proposed method offers a framework to optimize nanophotonic structures at the array level that is potentially applicable to a wide variety of objective functions and actively tunable metasurface antenna array platforms.

Tunable Metasurfaces: The Path to Fully Active Nanophotonics

“…As the individual meta‐atoms are designed to be tuned individually through the active ITO layer, an array‐level optimization technique could be performed to create high‐performance beam steering applications for any desired angle ( Figure a). [ 213 ] The phase profile can be matched after the fabrication of the metasurface through electrical bias to change the phase and amplitude profile, rather than adapting the meta‐atoms themselves. High‐directivity beam steering that was nonideal meta‐atoms was proved, even with a phase modulation range as small as 180°.…”

“…Adapted with permission. [ 213 ] Copyright 2020, American Chemical Society. b) 1‐bit programmable reflective metasurface.…”

Quasi‐Static and Time‐Modulated Optical Phased Arrays: Beamforming Analysis and Comparative Study

“…For this purpose, evolutionary optimization algorithms can serve as powerful tools due to their capability in identifying optimal solutions in large input parameter spaces. The inverse design approach introduced in the study by Thureja et al [ 35 ] relies on the optimization of the beam‐steering performance of the quasi‐static OPA by maximizing the directivity at the desired steering angle. This array‐level inverse design algorithm optimizes the covarying phase and amplitude values at each pixel through minimizing the amplitude modulation over the entire OPA, which reduces the sidelobe level and increases the directivity.…”

“…[ 32–34 ] In contrast to such topology optimization techniques, recently, a novel inverse design method is introduced for optimizing active metasurfaces, which aims at finding the optimal solutions in terms of functional characteristics such as the amplitude and phase of the geometrically fixed individual pixels in response to the external stimuli. [ 35 ] In this approach, the objective functions are defined to be the scattering properties of the reconfigurable pixels that are optimized to the desired values through exhaustive scanning within the input parameter space, without any change in the geometric configuration of the individual components. Inverse design of the large‐scale metasurfaces in the array level are also demonstrated in various studies, [ 35,36 ] which are applied to optimize the array architecture of the metasurfaces within high‐dimensional input parameter space.…”

“…[ 35 ] In this approach, the objective functions are defined to be the scattering properties of the reconfigurable pixels that are optimized to the desired values through exhaustive scanning within the input parameter space, without any change in the geometric configuration of the individual components. Inverse design of the large‐scale metasurfaces in the array level are also demonstrated in various studies, [ 35,36 ] which are applied to optimize the array architecture of the metasurfaces within high‐dimensional input parameter space. This can lead to expensive computational cost and complexity.…”