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：对半导体材料和器件缺陷的高效、准确检测方法需求日益增长。这些缺陷可能导致关键性故障和晶圆良率受限，对制造工艺效率产生不利影响。随着节点与图形尺寸不断缩小，即使采用扫描电子显微镜等高分辨率成像技术，由于需在灵敏度阈值附近运行，且不同底层或光刻胶材料物理性质存在差异，所获图像仍可能存在噪声。这种固有噪声是缺陷检测面临的主要挑战之一。机器学习算法作为有前景的解决方案，可通过训练准确分类并定位半导体样本中的缺陷。近年来，卷积神经网络在该领域展现出显著优势。本系统综述全面阐述了基于SEM图像的半导体缺陷自动检测技术现状，涵盖最新创新与进展。我们从IEEE Xplore和SPIE数据库中遴选出38篇相关文献，逐篇总结其应用场景、方法体系、数据集、实验结果、局限性与未来研究方向，并对各方法的综合特征进行了详细分析。最后，本文提出了基于SEM的缺陷检测领域未来具有前景的研究方向。