Novel methods for locating and matching IC cells based on standard cell libraries

In the domain of hardware assurance, reverse engineering (RE) is essential for ensuring the security and reliability of integrated circuits (ICs). A potential approach in the crucial step of netlist extraction involves matching patterns in IC images to standard cell libraries. However, the morphological variations in images of cells and intra-cell similarities present significant challenges to effective matching. This paper introduces a new matching dataset of cells in Scanning Electron Microscopy (SEM) images and standard cells, including 579 SEM cells, two standard cell libraries, and 508 types of standard cells. Furthermore, we propose a novel matching method reliant on standard cell libraries. This method generates templates using the feature information of standard cells and conducts matching by comparing the similarity between the feature vector sets of an SEM cell and the templates, achieving a 100% accuracy rate on the matching dataset. Given that the matching method relies on accurate cell localization, we propose two methods of merging bounding boxes. These methods can convert the object detector's detection results on patches into localization results on the entire image, achieving 99.48% accuracy and 99.31% recall on the image of the matching dataset. Finally, We consolidate these methods into a comprehensive workflow for automating the extraction of cell information in large-scale IC images.