Accurate inverse design of Fabry–Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network  

作　　者：PENG DAI YASI WANG YUEQIANG HU C.H.DE GROOT OTTO MUSKENS HUIGAO DUAN RUOMENG HUANG 

机构地区：[1]School of Electronics and Computer Science,University of Southampton,Southampton SO171BJ,UK [2]National Engineering Research Center for High Efficiency Grinding,College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China [3]School of Physics and Astronomy,University of Southampton,Southampton SO171BJ,UK

出　　处：《Photonics Research》2021年第5期I0069-I0079,共11页光子学研究（英文版）

基　　金：International Exchange Scheme(IEC\NSFC\170193)between Royal Society(UK);the National Natural Science Foundation of China(China)。

摘　　要：Structural color based on Fabry–Perot(F-P) cavity enables a wide color gamut with high resolution at submicroscopic scale by varying its geometrical parameters. The ability to design such parameters that can accurately display the desired color is therefore crucial to the manufacturing of F-P cavities for practical applications.This work reports the first inverse design of F-P cavity structure using deep learning through a bidirectional artificial neural network. It enables the production of a significantly wider coverage of color space that is over 215% of sRGB with extremely high accuracy, represented by an average ΔE_(2000) value below 1.2. The superior performance of this structural color-based neural network is directly ascribed to the definition of loss function in the uniform CIE 1976-Lab color space. Over 100,000 times improvement in the design efficiency has been demonstrated by comparing the neural network to the metaheuristic optimization technique using an evolutionary algorithm when designing the famous painting of "Haystacks, end of Summer" by Claude Monet. Our results demonstrate that, with the correct selection of loss function, deep learning can be very powerful to achieve extremely accurate design of nanostructured color filters with very high efficiency.

关 键 词：neural artificial CAVITY 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN15[自动化与计算机技术—控制科学与工程]