Optical Element Surface Defect Size Recognition Based on Decision Regression Tree

Abstract

Defect size recognition is significant to the evaluation of optical element surface quality. Currently, it’s mainly achieved by the conventional image process, such as threshold segmentation. However, as the defect size gradually approaches the diffraction limit of the imaging system, the defect gray distribution changes from bimodal to unimodal, which makes it difficult to be accurately recognized. In this paper, an electromagnetic simulation model of the microscopic scattering dark-field imaging (MSDI) system is built based on the finite-difference time-domain (FDTD) method to research the defect imaging mechanism. The point spread function (PSF) of our MSDI system is measured to revise the far-field simulation light intensity distribution, and the mean value of the distance between three groups of feature points, whose intensity is 0.75, 0.5, and 0.25 of the light intensity distribution peak value, is taken as the feature parameter of the light intensity distribution. To obtain the defect size, the decision regression tree (DRT) is proposed to get the relationship between the feature parameter and the defect size. Besides, some scratches samples are made to verify the validity of the DRT. The results show the relative error of DRT is within 10%, which is better than the threshold segmentation.