Non-contact automated defect detection using a deep learning approach in diffraction phase microscopy

Abstract

Precision measurement of defects from optical fringe patterns is a problem of significant practical relevance in non-destructive metrology. In this paper, we propose a robust deep learning approach based on atrous convolution neural network model for defect detection from noisy fringe patterns obtained in diffraction phase microscopy. The model utilizes the wrapped phase obtained from the fringe pattern as an input and generates a binary image depicting the defect and non-defect regions as output. The effectiveness of the proposed approach is validated through numerical simulations of various defects under different noise levels. Furthermore, the practical application of the proposed technique for identifying defects in diffraction phase microscopy experiments is also confirmed.