  In this study, we applied the PointRend (Point-based Rendering) method to
semiconductor defect segmentation. PointRend is an iterative segmentation
algorithm inspired by image rendering in computer graphics, a new image
segmentation method that can generate high-resolution segmentation masks. It
can also be flexibly integrated into common instance segmentation
meta-architecture such as Mask-RCNN and semantic meta-architecture such as FCN.
We implemented a model, termed as SEMI-PointRend, to generate precise
segmentation masks by applying the PointRend neural network module. In this
paper, we focus on comparing the defect segmentation predictions of
SEMI-PointRend and Mask-RCNN for various defect types (line-collapse, single
bridge, thin bridge, multi bridge non-horizontal). We show that SEMI-PointRend
can outperforms Mask R-CNN by up to 18.8% in terms of segmentation mean average
precision.
