Machine learning enabled inverse design of structural colour

Peng Dai; Kai Sun; Otto L. Muskens; C. H. (Kees) de Groot; Ruomeng Huang

doi:10.1117/12.3017690

18 June 2024 Machine learning enabled inverse design of structural colour

Peng Dai, Kai Sun, Otto L. Muskens, C. H. (Kees) de Groot, Ruomeng Huang

Proceedings Volume 13017, Machine Learning in Photonics; 130170C (2024) https://doi.org/10.1117/12.3017690
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Structural colour filters can display various colours by selectively transmitting or reflecting a specific wavelength by varying structural parameters rather than material components in the visible region. An important aspect of structural colour is the ability to design a structure that can accurately display the desired colour. While the conventional trial-anderror method requires substantial prior knowledge of the structure together with a number of simulations, deep learning provides an alternative way to inverse design the structural colour with high efficiency and accuracy. In this abstract, we will be discussing the deep learning enabled inverse design of structural colour. By employing the conditional generative adversarial networks (cGAN) to inverse design the structural colour, the one-to-many problem that is often encountered in nanophotonic inverse design is fully tackled. Moreover, we will also explore the possibility of applying this system to the dynamic structural colour inverse design.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Peng Dai, Kai Sun, Otto L. Muskens, C. H. (Kees) de Groot, and Ruomeng Huang "Machine learning enabled inverse design of structural colour", Proc. SPIE 13017, Machine Learning in Photonics, 130170C (18 June 2024); https://doi.org/10.1117/12.3017690

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