Improved bidirectional networks for nanostructure color design

Abstract

Localized optical resonances in silicon nanostructures have been increasingly used in color printing. By changing the geometric parameters of the silicon nanostructures to obtain different structural colors, it is possible to get a larger coverage range than the sRGB color gamut in the CIE color space, and achieve ultra-high-resolution color printing. However, the design of specific colors involves iterative optimization of geometric parameters, which is computationally expensive. Thus, it is very challenging to obtain millions of different colors in the color space. In this paper, we trained a feature-crossed neural network with attention mechanism to predict the structural color produced by random silicon nano truncated cones with high accuracy. On the problem of inverse design, we improve the loss function of the tandem network, which solves the non-uniqueness problem in the inverse design process and avoids the tandem net from falling into the wrong solution space. Our model can accurately predict millions of different color points in the CIE color gamut. In addition, the proposed methods can be easily extended to solve the optimization design problems in the field of nanophotonics.