Fast irradiance evaluation of freeform illumination lenses based on deep learning

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.