@inproceedings{ef71af4f021d499698bc027ab1c6429e,
title = "Fast irradiance evaluation of freeform illumination lenses based on deep learning",
abstract = "Compared with traditional optical surfaces, freeform surfaces provide much more degrees of freedom to tailor the irradiance distributions of light sources, forming previously unimaginable illumination optical systems. However, the complexity of freeform surfaces presents a huge challenge to the design process, especially when the light source size is assignable. We achieved fast irradiance evaluation of freeform illumination lens based on deep learning methods, preparing for a rapid optimization for the lens design. These learned simulation results are similar to those of LightTools, while the computation time is greatly reduced. The representation of freeform surfaces, the generation of datasets, and the selection of neural network structures are introduced in this paper. In the future, we will further improve the neural network performance and use the back-propagation of the neural network to realize a rapid optimization of the freeform lens.",
keywords = "deep learning, freeform optics, illumination, simulation",
author = "Haisong Tang and Zexin Feng and Dewen Cheng and Yongtian Wang",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2022 Applied Optics and Photonics China: Novel Optical Design; and Optics Ultra Precision Manufacturing and Testing, AOPC 2022 ; Conference date: 18-12-2022 Through 19-12-2022",
year = "2023",
doi = "10.1117/12.2652055",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Lingbao Kong and Dawei Zhang and Zexin Feng",
booktitle = "AOPC 2022",
address = "United States",
}