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.

翻译：随着半导体材料和器件缺陷检测高效精准方法的需求日益增长。这些缺陷会导致关键故障和晶圆良率限制，从而对制造过程效率产生不利影响。随着节点和图案尺寸不断缩小，即便采用扫描电子显微镜等高分辨率成像技术，由于需在灵敏度极限附近运行以及不同底层或抗蚀材料的物理特性差异，生成的图像仍存在噪声。这种固有噪声是缺陷检测面临的主要挑战之一。一种有前景的方法是采用机器学习算法，通过训练实现对半导体样本中缺陷的精准分类与定位。近年来，卷积神经网络在此领域展现出显著优势。本系统综述全面阐述了基于扫描电子显微镜图像的半导体缺陷自动检测技术现状，涵盖最新创新与发展。研究选取了IEEE Xplore与SPIE数据库中收录的38篇相关文献，系统总结了每项研究的应用场景、方法论、数据集、实验结果、局限性及未来工作方向，并对各方法进行了综合分析与比较。最后，本文提出了扫描电子显微镜缺陷检测领域具有潜力的未来研究方向。