Author:
Clini de Souza Arthur,Lanteri Stéphane,Hernández-Figueroa Hugo Enrique,Abbarchi Marco,Grosso David,Kerzabi Badre,Elsawy Mahmoud
Abstract
AbstractWe 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.
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. Bohren, C. F. & Huffman, D. R. Absorption and Scattering of Light by Small Particles (Wiley, 2008).
2. Kerker, M. The Scattering of Light and Other Electromagnetic Radiation (Elsevier, 2016).
3. Brown, M. A. & De Vito, S. C. Predicting azo dye toxicity. Crit. Rev. Environ. Sci. Technol. 23, 249–324 (1993).
4. Kim, H. et al. Structural colour printing using a magnetically tunable and lithographically fixable photonic crystal. Nat. Photonics 3, 534–540 (2009).
5. Chen, W. T. et al. High-efficiency broadband meta-hologram with polarization-controlled dual images. Nano Lett. 14, 225–230 (2014).
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