Generating off-axis reflective imaging systems consisting of flat phase elements based on deep-learning

Imaging systems consisting of flat phase elements can achieve more compactness and lighter-weight. In this paper, we propose a design framework of off-axis reflective imaging system consisting of flat phase elements based on deep-learning. Differential ray tracing for off-axis systems consisting of flat phase elements is used. Supervised and unsupervised learning are combined to improve the generalization ability of the deep neural network for a wide range of system and structure parameter values. Single or multiple systems can be generated directly after the design requirements are inputted into the network, and can be taken as good starting points for further optimization. The design efficiency can be significantly improved, and the dependence on the advanced design skills is dramatically reduced.