The growing complexity of semiconductor manufacturing at
advanced technology nodes has intensified the need for robust, automated defect
inspection methods. Design-based inspection, which compares scanning electron
microscope (SEM) images of fabricated wafers against their original
computer-aided design (CAD) layouts, offers a powerful approach for detecting
both systematic and random defects. However, the development and benchmarking
of such inspection algorithms is severely constrained by the scarcity of labelled
SEM defect datasets, which are expensive to produce and tightly guarded as
proprietary by fabrication facilities. This chapter presents an end-to-end,
open-source pipeline for generating synthetic paired CAD and SEM image datasets
with controllable, ground-truth-labelled defects, and for performing automated
defect detection through structural similarity (SSIM) analysis. The pipeline
comprises five modular stages: (1) synthetic layout generation in
industry-standard OASIS format, (2) paired CAD and SEM-like image rendering
with physically motivated degradation models, (3) configurable synthetic defect
injection, (4) phase-correlation-based image alignment followed by local SSIM
computation and morphological post-processing for defect mask extraction, and
(5) aggregate scoring and ranking of inspection sites by defect severity. The
paired image rendering generates, for each inspection site, a clean binary CAD
image and a SEM-like image with sequential physically motivated degradations
simulating realistic electron microscopy effects. The defect injection module
selectively modifies a configurable fraction of SEM images (default 10%) to
introduce synthetic defects. The detection stage processes each matched pair of
CAD and SEM images through four substeps: alignment, similarity mapping,
thresholding, and morphological cleanup. Experimental results on a generated
dataset of 200 image pairs demonstrate that the pipeline achieves a detection
F1-score of 0.93 under moderate noise conditions and degrades gracefully as
imaging noise increases. The fully reproducible, configurable nature of this
toolkit makes it suitable for algorithm benchmarking, machine learning model
pre-training, threshold optimisation studies, and educational demonstrations of
semiconductor inspection concepts.
Author(s) Details
Balachandar
Jeganathan
ASML, San Jose, CA, USA.
Please see the book here :- https://doi.org/10.9734/bpi/nhstc/v9/7212
