Structural colors designed by deep learning

Lu Wang; Tao Wang

doi:10.1117/12.2671706

10 April 2023 Structural colors designed by deep learning

Lu Wang, Tao Wang

Proceedings Volume 12614, 14th International Photonics and Optoelectronics Meetings (POEM 2022); 126141E (2023) https://doi.org/10.1117/12.2671706
Event: 14th International Photonics and Optoelectronics Meeting (POEM 2022), 2022, Wuhan, China

Abstract

Structural colors can be generated by metasurfaces with the capability of spectrum manipulation at subwavelength. In general, the optimization of specific color designs and iterative geometric parameters is computationally time-consuming, so obtaining thousands of different structural colors can be challenging. Deep learning methods offer a new approach to the efficient design of nanophotonic devices, as it revolutionizes the way nanophotonic devices are designed. Here, we trained a deep learning method, which can predict the colors by random geometries in the forward modeling process. The forward design model is good at processing data, which is an effective way to design nanophotonic devices.

Citation Download Citation

Lu Wang and Tao Wang "Structural colors designed by deep learning", Proc. SPIE 12614, 14th International Photonics and Optoelectronics Meetings (POEM 2022), 126141E (10 April 2023); https://doi.org/10.1117/12.2671706

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available