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:在此研究中,我们应用了半导体缺陷分解的点点(PointRendern)方法。点(PointRendering)是一种迭代分解算法,它受计算机图形图像转换的启发,这是一种新的图像分解法,可以产生高分辨率分解面罩。它也可以灵活地融入普通的例分解元结构,如Mask-RCNN和FCN等语义元结构。我们采用了一个称为SEMI-PointRend的模型,以通过应用点神经网络模块生成精确的分解面罩。在本文中,我们侧重于比较SEMI-PointRend和Mask-RCNN对各种缺陷类型(线折叠、单桥、薄桥、多桥非横向桥)的缺陷分解预测。我们表明,SEMI-PointRend在分解方面可以比Mask R-CNN高出18.8%的平均精确度。