Optical design of refractive optical system based on deep learning

Abstract

Aiming at the problems such as low efficiency of initial structure optimization design of traditional refractive optical system and overreliance on experience in structure selection. In this paper, an initial structure automatic optimization design method of refractive optical system based on deep learning is proposed. The structural characteristic data of the reference lens in the optical lens library are learned through supervised training. Unsupervised training model based on ray tracing is constructed to improve the generalization ability of deep neural network model. Through the network model generated by training, the optical system structure parameters including real glass are output, and the automatic optimal design of the initial structure of the refractive optical system is realized. The design results show that the initial structure spot radius of optical system in full field and full spectrum optimized by network model are close to the reference lens. The initial structure of the optical system can be designed according to different focal length requirements. The success rate of one million initial structures designed in this paper is greater than 96.403%, which indicates that the network model has good generalization ability. The method proposed in this paper contributes to automatically generate the initial structure of the refractive optical system rapidly and provides a new solution for the optimization of complex optical system.