Deep learning based defect classification and detection in SEM images: a mask R-CNN approach

Abstract

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Current state-of-the-art optical and e-beam inspection tools have certain limitations as these tools are often driven by some rule-based techniques for defect classification and detection. These tool/software limitations often lead to misclassification which necessitates manual classification. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method [1] for improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. This paper aims at detecting and segmenting different types of defect patterns such as bridges, breaks and line collapse as well as to differentiate accurately between multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.