Detecting and analyzing various defect types in semiconductor materials is an
important prerequisite for understanding the underlying mechanisms as well as
tailoring the production processes. Analysis of microscopy images that reveal
defects typically requires image analysis tasks such as segmentation and object
detection. With the permanently increasing amount of data that is produced by
experiments, handling these tasks manually becomes more and more impossible. In
this work, we combine various image analysis and data mining techniques for
creating a robust and accurate, automated image analysis pipeline. This allows
for extracting the type and position of all defects in a microscopy image of a
KOH-etched 4H-SiC wafer that was stitched together from approximately 40,000
individual images.

通过融合各种图像分析和数据挖掘技术，我们在这项工作中创建了一个强大且准确的自动化图像分析流水线，用于提取显微图像中 KOH 刻蚀的 4H-SiC 晶片的所有缺陷的类型和位置。

无监督与监督学习相结合的显微镜技术实现对整片 4H-SiC 晶片缺陷分析

Combining unsupervised and supervised learning in microscopy enables  defect analysis of a full 4H-SiC wafer

A growing need exists for efficient and accurate methods for detecting
defects in semiconductor materials and devices. These defects can have a
detrimental impact on the efficiency of the manufacturing process, because they
cause critical failures and wafer-yield limitations. As nodes and patterns get
smaller, even high-resolution imaging techniques such as Scanning Electron
Microscopy (SEM) produce noisy images due to operating close to sensitivity
levels and due to varying physical properties of different underlayers or
resist materials. This inherent noise is one of the main challenges for defect
inspection. One promising approach is the use of machine learning algorithms,
which can be trained to accurately classify and locate defects in semiconductor
samples. Recently, convolutional neural networks have proved to be particularly
useful in this regard. This systematic review provides a comprehensive overview
of the state of automated semiconductor defect inspection on SEM images,
including the most recent innovations and developments. 38 publications were
selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of
these, the application, methodology, dataset, results, limitations and future
work were summarized. A comprehensive overview and analysis of their methods is
provided. Finally, promising avenues for future work in the field of SEM-based
defect inspection are suggested.

半导体材料和器件中检测缺陷的高效准确方法需求日益增长。这篇文章通过对 38 篇相关研究论文进行系统回顾，全面总结了基于扫描电子显微镜图像的自动化半导体缺陷检测的现状，包括最新创新和发展，提供了方法的综合概述和分析，并对未来工作提出了有希望的方向。