集成电路反向工程中HE-UNet的IC图像分割算法研究

doi:10.19678/j.issn.1000-3428.0069935

摘要/Abstract

摘要：

芯片产业是国家安全和经济发展的重要基础, 而集成电路(IC)反向工程(RE)作为分析芯片内部性能的手段, 是芯片产业链中的重要环节。RE包括从扫描电子显微镜(SEM)逐层采集芯片图像、识别器件、提取栅极网表、推断其功能等步骤, 而将电气元件和金属线从IC图像背景中分割出来是识别器件等步骤的前提。然而, 传统图像分割方法因为缺乏专家经验的学习, 不能适应IC图像复杂多变的电路情况。为此, 提出一种HE-UNet方法, 用于提取IC图像中的金属线与过孔。HE-UNet包含3个步骤: 首先, 利用U-M2网络提取芯片图像的含噪特征; 其次, 利用霍夫圆检测算法去除过孔周围的噪声; 最后, 利用边缘检测池化去除远离过孔的噪声。在尺寸为1 024×1 024像素的IC图像上进行实验, 结果表明, HE-UNet可以有效完成金属线和过孔的分割, 其平均交并比(mIoU)为98.24%, 平均像素准确率(MPA)为99.11%, 均优于对比方法。

关键词: 集成电路, 反向工程, 图像分割, 霍夫圆检测, HE-UNet

Abstract:

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.

Key words: Integrated Circuit (IC), Reverse Engineering (RE), image segmentation, Hough circle detection, HE-UNet

程弘楠, 张晨光. 集成电路反向工程中HE-UNet的IC图像分割算法研究[J]. 计算机工程, 2026, 52(1): 196-205.

CHENG Hongnan, ZHANG Chenguang. Research on IC Image Segmentation Algorithm of HE-UNet in Integrated Circuit Reverse Engineering[J]. Computer Engineering, 2026, 52(1): 196-205.

https://www.ecice06.com/CN/Y2026/V52/I1/196

图/表 18

图1 IC图像的主要阴影噪声

Fig.1 The main shadow noise of IC image

图2 本文方法的框架

Fig.2 The framework of the method in this paper

图3 U-M2网络结构

Fig.3 U-M2 network structure

图4 Bottleneck组件结构

Fig.4 Bottleneck component structure

图5 去除过孔周围噪声的操作流程

Fig.5 Operation process for removing noise around via holes

图6 霍夫圆检测过程

Fig.6 Hough circle detection procedure

图7 边缘检测池化的原理和效果

Fig.7 The principle and effect of edge detection pooling

图8 边缘函数Border

Fig.8 Edge function Border

图9 边缘检测池化效果

Fig.9 Edge detection pooling effect

图10 本文网络的主要流程

Fig.10 The main procedure of this network

图11 背景像素值相差较大的IC图像

Fig.11 IC images with large differences in background pixel values

图12 U-Net结合不同步骤所产生的效果

Fig.12 The effect generated by combining different steps of U-Net

图13 不同方法的去噪效果

Fig.13 The denoising effect of different methods

图14 不同网络的分割效果

Fig.14 Segmentation effect of different networks

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