A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images.

This paper proposes an automated data-driven integrated circuit segmentation approach of scan electron microscopy (SEM) images inspired by state-of-the-art CNN-based image perception methods. Based on the requirements derived from real industry applications, we take wire segmentation and via detection algorithms to generate integrated circuit segmentation maps from SEMs in our approach. On SEM images collected in the industrial applications, our method achieves an average of 50.71 on Electrically Significant Difference (ESD) in the wire segmentation task and 99.05% F1 score in the via detection task, which achieves about 85% and 8% improvements over the reference method, respectively.