In semiconductor manufacturing, lithography has often been the manufacturing
step defining the smallest possible pattern dimensions. In recent years,
progress has been made towards high-NA (Numerical Aperture) EUVL
(Extreme-Ultraviolet-Lithography) paradigm, which promises to advance pattern
shrinking (2 nm node and beyond). However, a significant increase in stochastic
defects and the complexity of defect detection becomes more pronounced with
high-NA. Present defect inspection techniques (both non-machine learning and
machine learning based), fail to achieve satisfactory performance at high-NA
dimensions. In this work, we investigate the use of the Slicing Aided Hyper
Inference (SAHI) framework for improving upon current techniques. Using SAHI,
inference is performed on size-increased slices of the SEM images. This leads
to the object detector's receptive field being more effective in capturing
small defect instances. First, the performance on previously investigated
semiconductor datasets is benchmarked across various configurations, and the
SAHI approach is demonstrated to substantially enhance the detection of small
defects, by approx. 2x. Afterwards, we also demonstrated application of SAHI
leads to flawless detection rates on a new test dataset, with scenarios not
encountered during training, whereas previous trained models failed. Finally,
we formulate an extension of SAHI that does not significantly reduce
true-positive predictions while eliminating false-positive predictions.

半导体制造中的光刻术通常是界定最小模式尺寸的制造步骤，在最近几年中，高数值孔径极紫外光刻术 (high-NA EUVL) 的发展取得了进展，承诺推动模式缩小 (2 纳米节点及以上)。然而，高数值孔径导致了随机缺陷的显著增加和缺陷检测的复杂性增加。本研究探讨了利用切片辅助的超推理 (SAHI) 框架改进当前技术的方法。通过在扫描电子显微镜 (SEM) 图像的增大尺寸切片上进行推理，改进了目标检测器的感知域，提高了小缺陷实例的检测。首先，在以前研究过的半导体数据集上对各种配置进行了基准测试，并且证明了 SAHI 方法可以将小型缺陷的检测能力提高约 2 倍。接下来，我们还展示了 SAHI 的应用可以在新的测试数据集上实现完美的检测率，即使在训练过程中未遇到的情况下，之前训练的模型也无法做到。最后，我们提出了 SAHI 的扩展版本，即在不明显降低真阳性预测的情况下，消除了假阳性预测。