Metasurfaces designed by a bidirectional deep neural network and iterative algorithm for generating quantitative field distributions

Abstract

Metasurfaces, which are the two-dimensional counterparts of metamaterials, have demonstrated unprecedented capabilities to manipulate the wavefront of electromagnetic waves in a single flat device. Despite various advances in this field, the unique functionalities achieved by metasurfaces have come at the cost of the structural complexity, resulting in a time-consuming parameter sweep for the conventional metasurface design. Although artificial neural networks provide a flexible platform for significantly improving the design process, the current metasurface designs are restricted to generating qualitative field distributions. In this study, we demonstrate that by combining a tandem neural network and an iterative algorithm, the previous restriction of the design of metasurfaces can be overcome with quantitative field distributions. As proof-of-principle examples, metalenses predicted via the designed network architecture that possess multiple focal points with identical/orthogonal polarisation states, as well as accurate intensity ratios (quantitative field distributions), were numerically calculated and experimentally demonstrated. The unique and robust approach for the metasurface design will enable the acceleration of the development of devices with high-accuracy functionalities, which can be applied in imaging, detecting, and sensing.

Keywords

• metasurfaces
• bidirectional deep neural network
• iterative algorithm
• focal points
• vortex