| Full text | |
| Author(s): |
de Souza, Arthur Clini
;
Lanteri, Stephane
;
Hernandez-Figueroa, Hugo Enirique
;
Abbarchi, Marco
;
Grosso, David
;
Kerzabi, Badre
;
Elsawy, Mahmoud
Total Authors: 7
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 13, n. 1, p. 9-pg., 2023-12-04. |
| 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. (AU) | |
| FAPESP's process: | 21/11380-5 - CPTEn - São Paulo Center for the Study of Energy Transition |
| Grantee: | Luiz Carlos Pereira da Silva |
| Support Opportunities: | Research Grants - Science Centers for Development |
| FAPESP's process: | 21/06506-0 - Strongly resonant all-dielectric metasurfaces based on quasi-dark and toroidal modes |
| Grantee: | Hugo Enrique Hernández Figueroa |
| Support Opportunities: | Regular Research Grants |