Research on IC Image Segmentation Algorithm of HE-UNet in Integrated Circuit Reverse Engineering

The chip industry is critical for national security and economic development, and Integrated Circuit (IC) Reverse Engineering (RE), as a means of analyzing the internal performance of chips, is an important link in the chip industry chain. RE includes steps such as layer-by-layer acquisition of chip images using Scanning Electron Microscopy (SEM), identification of devices, extraction of gate netlists, and inference of their functions. Segmentation of electrical components and metal lines from the IC image background is a prerequisite for identifying devices and other steps. However, traditional image segmentation methods cannot adapt to the complex and ever-changing circuit conditions of IC images owing to the lack of expert experience in learning. To this end, the HE-UNet method is proposed for extracting metal lines and vias from IC images. HE-UNet consists of three steps: first, the U-M2 network is used to extract noisy features from chip images; second, the Hough circle detection algorithm is used to remove noise around the via holes; and third, edge detection pooling is used to remove noise from the via holes. Experiments conducted on IC images with a size of 1 024×1 024 pixels reveal that HE-UNet can effectively segment metal lines and vias, with a mean Intersection over Union (mIoU) of 98.24% and Mean Pixel Accuracy (MPA) of 99.11%, both of which are superior to those achieved by other methods.