基于图族建模与图神经网络的热点检测算法

梁珑曦; 蔡述庭; 张慧

doi:10.12052/gdutxb.250111

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基于图族建模与图神经网络的热点检测算法

集成电路科学与工程 | 更新时间：2026-05-27

- 基于图族建模与图神经网络的热点检测算法
- A Hotspot Detection Algorithm Based on Graph Family Modelling and Graph Neural Networks
- 广东工业大学学报 2025年42卷第6期页码：34-43
- 作者机构：
  
  1. 广东工业大学集成电路学院,广东,广州,510006
  2. 广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  梁珑曦(2003–) ，男，硕士研究生，主要研究方向为人工智能辅助电子设计自动化，E-mail:lianglongxi@me.com
  张慧(1979–) ，女，讲师，博士，主要研究方向为嵌入式系统设计、智能控制、深度学习，E-mail:zh2008@gdut.edu.cn
- 基金信息：
  
  广东省重点领域研发计划项目(2022B0701180001)
- DOI：10.12052/gdutxb.250111
  中图分类号： TN407;TN707
- 纸质出版：2025
- 稿件说明：
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梁珑曦, 蔡述庭, 张慧. 基于图族建模与图神经网络的热点检测算法[J]. 广东工业大学学报, 2025, 42(6): 34–43.

Liang Longxi, Cai Shuting, Zhang Hui. A Hotspot Detection Algorithm Based on Graph Family Modelling and Graph Neural Networks[J]. 2025, 42(6): 34-43.
梁珑曦, 蔡述庭, 张慧. 基于图族建模与图神经网络的热点检测算法[J]. 广东工业大学学报, 2025, 42(6): 34–43. DOI： 10.12052/gdutxb.250111

Liang Longxi, Cai Shuting, Zhang Hui. A Hotspot Detection Algorithm Based on Graph Family Modelling and Graph Neural Networks[J]. 2025, 42(6): 34-43. DOI： 10.12052/gdutxb.250111.

摘要

随着集成电路工艺推进至先进节点，版图结构复杂度大幅上升，制造良率维持难度显著增加，对光刻热点检测提出更高要求。针对现有检测方法在版图建模表达力、增强样本区分性与模型检测能力方面的问题，本文提出一种基于图族建模与图神经网络的热点检测算法，实现结构表达力与模型判别性能的协同提升。首先，面向光刻版图片段提出一种多尺度图族建模方法，通过局部子图与全局大图联合构建图族结构，能有效提升版图结构与语义属性的表达能力，同时增强样本在数据增强处理下的可分性。其次，在图族结构作为样本输入的基础上，设计了分层图神经网络热点检测模型，提升了多层次特征提取能力与对核心区域的结构聚焦能力。最后，本文图族建模方法的t-SNE嵌入可视化实验结果表明，在样本分布均匀性与结构区分性方面具有显著优势，相比传统图像建模，其平均最近邻距离(Mean Nearest Neighbor Distance，MNND) 提升了206个百分点、Shannon分布熵提升了16个百分点。本文图神经网络模型使用ICCAD'19 TNSB竞赛数据集进行测试，在保持结构层次化的同时实现了优异的检测效果，召回率达到99.91%，误报率仅为1.12%，为光刻版图片段的数据增强表达能力、复杂热点感知能力的提升与光刻热点检测问题的解决提供创新算法。

Abstract

The rapid scaling of semiconductor technologies has led to increasingly complex integrated circuit layouts

which pose severe challenges to maintaining manufacturing yield and highlight the urgent need for accurate lithographic hotspot detection. Existing detection methods are often constrained by limited layout modelling capacity

insufficient separability of augmented samples

and suboptimal classification performance. To overcome these challenges

a hotspot detection algorithm was developed that combined a multi-scale graph family modelling strategy with a hierarchical graph neural network. The proposed graph family model constructed both local subgraphs and a global graph to represent geometric details and contextual semantics simultaneously

thereby improving structural representation and preserving data separability under various augmentation operations. On this basis

a hierarchical GNN was designed to extract multi-level features and strengthen the model’s focus on critical core regions. Experimental evaluations on the ICCAD'19 TNSB benchmark demonstrate that the proposed method substantially improves data distribution balance

achieving a 206 percentage point increase in MNND and a 16 percentage point gain in Shannon entropy. Moreover

the detection model attains a recall of 99.91% with only a 1.12% false alarm rate

outperforming state-of-the-art alternatives. These results confirm that the proposed graph-based approach offers both strong structural expressiveness and robust detection capability

providing a promising solution for addressing the challenges of lithographic hotspot detection in advanced technology nodes.

关键词

Keywords

references

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