As semiconductor patterning dimensions shrink, more advanced Scanning
Electron Microscopy (SEM) image-based defect inspection techniques are needed.
Recently, many Machine Learning (ML)-based approaches have been proposed for
defect localization and have shown impressive results. These methods often rely
on feature extraction from a full SEM image and possibly a number of regions of
interest. In this study, we propose a deep Reinforcement Learning (RL)-based
approach to defect localization which iteratively extracts features from
increasingly smaller regions of the input image. We compare the results of 18
agents trained with different feature extractors. We discuss the advantages and
disadvantages of different feature extractors as well as the RL-based framework
in general for semiconductor defect localization.

该研究通过使用深度强化学习方法，从逐渐缩小的输入图像区域中迭代地提取特征，比较了使用不同特征提取器训练的 18 个代理商的结果，并讨论了不同特征提取器的优缺点以及针对半导体缺陷定位的深度强化学习框架的一般优势和劣势。