Deep learning assisted non-contact defect identification method using diffraction phase microscopy.

Abstract

Reliable detection of defects from optical fringe patterns is a crucial problem in non-destructive optical interferometric metrology. In this work, we propose a deep-learning-based method for fringe pattern defect identification. By attributing the defect information to the fringe pattern's phase gradient, we compute the spatial phase derivatives using the deep learning model and apply the gradient map to localize the defect. The robustness of the proposed method is illustrated on multiple numerically synthesized fringe pattern defects at various noise levels. Further, the practical utility of the proposed method is substantiated for experimental defect identification in diffraction phase microscopy.