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.

翻译：在本研究中，我们将PointRend（基于点的渲染）方法应用于半导体缺陷分割。PointRend是一种受计算机图形学中图像渲染启发的迭代分割算法，作为一种新型图像分割方法，能够生成高分辨率的分割掩码。它还可灵活集成到诸如Mask-RCNN等常见实例分割元架构以及FCN等语义分割元架构中。我们实现了一个名为SEMI-PointRend的模型，通过应用PointRend神经网络模块生成精准的分割掩码。本文聚焦于对比SEMI-PointRend与Mask-RCNN在多种缺陷类型（线塌陷、单桥、薄桥、非水平多桥）上的分割预测结果。研究表明，在分割平均精度方面，SEMI-PointRend相较于Mask R-CNN最高提升18.8%。