Detecting defects in the inspection stage of semiconductor manufacturing process is a crucial task to improve yield and productivity as well as wafer quality. Recent Advances in semiconductor process technology have greatly increased the transistor density. As a result, an increasingly high number of defects inevitably emerge and we need a more accurate and efficient detection method to manage them. In this paper, we propose a deep-learning-based defect detection model to expedite the process. It adopts an adversarial network architecture of conditional GAN. The discriminator of an adversarial network architecture helps the detection model learn to detect and classify defects accurately. The high performance is achieved by using Focal Loss, PixelGAN and multi-scale level features, which is shown to be better than the baseline model, CenterNet, when tested for a real industrial dataset.

中文翻译：

---

半导体制造过程中的对抗性缺陷检测

在半导体制造过程的检测阶段检测缺陷是提高良率和生产力以及晶圆质量的关键任务。半导体工艺技术的最新进展极大地增加了晶体管密度。因此，不可避免地会出现越来越多的缺陷，我们需要一种更准确、更有效的检测方法来管理它们。在本文中，我们提出了一种基于深度学习的缺陷检测模型来加快这一过程。它采用了条件 GAN 的对抗网络架构。对抗网络架构的鉴别器帮助检测模型学习准确检测和分类缺陷。高性能是通过使用 Focal Loss、PixelGAN 和多尺度级别特征来实现的，这被证明优于基线模型 CenterNet，