  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.
