Segmentation of IC Images in Integrated Circuit Reverse Engineering Using EfficientNet Encoder Based on U-Net++ Architecture

Segmentation of electrical components and metal traces from integrated circuit (IC) images is crucial for IC reverse engineering. Existing image segmentation methods face significant challenges when applied to IC images, including high resolution, limited training data, and the need for precise segmentation. To address these issues, this study proposes a combined approach of segmentation and post-processing. During the segmentation stage, we use UNet++ as the base architecture, with EfficientNet-B7 as the encoder, resulting in an E-UNet++ model. This model effectively combines the efficiency and pre-training capabilities of EfficientNet with the ability of UNet++ to capture both global structural information and fine-grained boundary details in IC images, enabling it to effectively handle challenges such as high resolution and limited training samples. In the post-processing stage, to address potential noise caused by the insufficient utilization of spatial location information in network-based methods, we propose the use of Hough circle detection and median filtering to eliminate noise from vias and non-via regions. Compared to the suboptimal segmentation model, our proposed method achieved a 0.58% improvement in mean intersection over union (mIoU) and a 0.33% improvement in mean pixel accuracy (MPA) on the real-world dataset and a 0.78% improvement in mIoU and a 0.44% improvement in MPA on the open-source dataset. These experimental results demonstrate that our method effectively improves the accuracy of IC segmentation.