Computational modeling demonstrates inverse design of structural color filters exceeding the sRGB gamut, highlighting deep learning utility in nanophotonics.
The present work showcases an innovative optimization methodology based on deep learning that combines Multi- Valued Artificial Neural Networks and back-propagation optimization. The methodology addresses the inherent limitations of conventional approaches when employed in isolation. We applied the proposed methodology to design structural color filters that surpasses the sRGB gamut while preserving fabrication constraints.
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Souza et al. (2024) studied this question.
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