  In this paper, we present a YOLO-based framework for layout hotspot
detection, aiming to enhance the efficiency and performance of the design rule
checking (DRC) process. Our approach leverages the YOLOv8 vision model to
detect multiple hotspots within each layout image, even when dealing with large
layout image sizes. Additionally, to enhance pattern-matching effectiveness, we
introduce a novel approach to augment the layout image using information
extracted through Principal Component Analysis (PCA). The core of our proposed
method is an algorithm that utilizes PCA to extract valuable auxiliary
information from the layout image. This extracted information is then
incorporated into the layout image as an additional color channel. This
augmentation significantly improves the accuracy of multi-hotspot detection
while reducing the false alarm rate of the object detection algorithm. We
evaluate the effectiveness of our framework using four datasets generated from
layouts found in the ICCAD-2019 benchmark dataset. The results demonstrate that
our framework achieves a precision (recall) of approximately 83% (86%) while
maintaining a false alarm rate of less than 7.4\%. Also, the studies show that
the proposed augmentation approach could improve the detection ability of
never-seen-before (NSB) hotspots by about 10%.
