Key Laboratory for Organic Electronics and Information Displays (KLOEID), Institute of Advanced Materials (IAM), School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Adv Mater. 2019 Dec;31(51):e1905467. doi: 10.1002/adma.201905467. Epub 2019 Nov 7.
Silicon nanostructure color has achieved unprecedented high printing resolution and larger color gamut than sRGB. The exact color is determined by localized magnetic and electric dipole resonance of nanostructures, which are sensitive to their geometric changes. Usually, the design of specific colors and iterative optimization of geometric parameters are computationally costly, and obtaining millions of different structural colors is challenging. Here, a deep neural network is trained, which can accurately predict the color generated by random silicon nanostructures in the forward modeling process and solve the nonuniqueness problem in the inverse design process that can accurately output the device geometries for at least one million different colors. The key results suggest deep learning is a powerful tool to minimize the computation cost and maximize the design efficiency for nanophotonics, which can guide silicon color manufacturing with high accuracy.
硅纳米结构的颜色实现了比 sRGB 更高的打印分辨率和更大的色域。颜色的精确性取决于纳米结构的局域磁和电偶极子共振，而共振对其几何变化非常敏感。通常，特定颜色的设计和几何参数的迭代优化计算成本很高，获得数百万种不同的结构颜色具有挑战性。在这里，训练了一个深度神经网络，可以在正向建模过程中准确预测随机硅纳米结构产生的颜色，并解决逆设计过程中的非唯一性问题，该问题可以准确地输出至少一百万个不同颜色的器件几何形状。关键结果表明，深度学习是一种强大的工具，可以最小化计算成本并最大限度地提高纳米光子学的设计效率，从而以高精度指导硅颜色制造。
