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In this research work, we have demonstrated the application of Mask-RCNN(Regional Convolutional Neural Network), a deep-learning algorithm for computervision and specifically object detection, to semiconductor defect inspectiondomain. Stochastic defect detection and classification during semiconductormanufacturing has grown to be a challenging task as we continuously shrinkcircuit pattern dimensions (e.g., for pitches less than 32 nm). Defectinspection and analysis by state-of-the-art optical and e-beam inspection toolsis generally driven by some rule-based techniques, which in turn often causesto misclassification and thereby necessitating human expert intervention. Inthis work, we have revisited and extended our previous deep learning-baseddefect classification and detection method towards improved defect instancesegmentation in SEM images with precise extent of defect as well as generatinga mask for each defect category/instance. This also enables to extract andcalibrate 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 asto calculate the surface area in terms of pixels. We are aiming at detectingand segmenting different types of inter-class stochastic defect patterns suchas bridge, break, and line collapse as well as to differentiate accuratelybetween intra-class multi-categorical defect bridge scenarios (asthin/single/multi-line/horizontal/non-horizontal) for aggressive pitches aswell as thin resists (High NA applications). Our proposed approach demonstratesits effectiveness both quantitatively and qualitatively.

Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach