  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. Stochastic 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). Defect
inspection and analysis by state-of-the-art optical and e-beam inspection tools
is generally driven by some rule-based techniques, which in turn often causes
to misclassification and thereby necessitating human expert intervention. In
this work, we have revisited and extended our previous deep learning-based
defect classification and detection method towards 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. We are aiming at detecting
and segmenting different types of inter-class stochastic defect patterns such
as bridge, break, and line collapse as well as to differentiate accurately
between intra-class 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.
