Back-Propagation Optimization and Multi-Valued Artificial Neural Networks for Highly Vivid Structural Color Filter Metasurfaces

TL;DR

This paper presents a deep learning-based inverse design method for highly efficient, vivid structural color filter metasurfaces, outperforming previous approaches and enabling broader applications in metasurface design.

Contribution

It introduces a novel combination of Multi-Valued Artificial Neural Networks and back-propagation optimization for designing superior color filter metasurfaces.

Findings

Achieved highly vivid colors surpassing sRGB gamut.

Outperformed all dataset configurations in efficiency.

Demonstrated extension to various metasurface functionalities.

Abstract

We introduce a novel technique for designing color filter metasurfaces using a data-driven approach based on deep learning. Our innovative approach employs inverse design principles to identify highly efficient designs that outperform all the configurations in the dataset, which consists of 585 distinct geometries solely. By combining Multi-Valued Artificial Neural Networks and back-propagation optimization, we overcome the limitations of previous approaches, such as poor performance due to extrapolation and undesired local minima. Consequently, we successfully create reliable and highly efficient configurations for metasurface color filters capable of producing exceptionally vivid colors that go beyond the sRGB gamut. Furthermore, our deep learning technique can be extended to design various pixellated metasurface configurations with different functionalities.