Enhancing Layout Hotspot Detection Efficiency with YOLOv8 and PCA-Guided Augmentation

TL;DR

This paper introduces a YOLOv8-based layout hotspot detection framework enhanced with PCA-guided image augmentation, significantly improving detection accuracy and reducing false alarms in large layout images.

Contribution

The paper proposes a novel PCA-based augmentation method integrated with YOLOv8 to improve layout hotspot detection accuracy and efficiency, especially for large images.

Findings

Achieves approximately 83% precision and 86% recall.

Reduces false alarm rate to less than 7.4%.

Improves detection of unseen hotspots by about 10%.

Abstract

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%.