Deep neural network based calibration for freeform surface misalignments in general interferometer.

Abstract

In interferometric testing for optical freeform surfaces, the calibration of surface misalignment aberrations is a tremendous challenge. A complex surface figure with a six-axis degree of freedom introduces complex non-linear misaligned aberration variations with slight misalignments. The traditional sensitive matrix method provides considerable precision in only the linear area, which is unacceptable. In this paper, a deep neural network (DNN)-based calibration method is introduced. The well-trained DNN can treat this non-linear relation and, thus, yield accurate misalignment estimation. Subsequently, the estimated misalignments are simulated in a model to predict all the misalignment aberrations by ray tracing. These aberrations are then removed by a simple wavefront data subtraction. The simulation and experimental results show the feasibility of the proposed DNN-based method.