arXiv cs.AI· Yu Feng, Liyi Zhou, Haoyu Wang, Yiming Xu, Hao Geng, Shaozhi Deng, Ningsheng Xu, Shisheng Xiong·· 2 天前AI 评分16
GNN 引导局部精修：面向 DSA-MP 的高效版图分解方法
GNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP
AI 导读
研究提出一种新的冲突图模型，将此前被忽略的 DSA 模板边到边间距约束纳入其中，并用图神经网络驱动的局部精修策略实现高效可扩展的版图分解。实验显示该方法相比 SOTA 方法解质量更高、收敛更快，并在不同版图规模、设计风格、工艺节点和掩模数量下保持稳健。该工作已被 ICCAD 2026 接收。
正文 · 原文
Abstract:Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature sizes shrink. Directed self-assembly (DSA) offers a cost-effective complement, extending the resolution of optical lithography to sub-10 nm and beyond. Integrating DSA into MPL (DSA-MP) further enhances the resolution of contact/via patterning. Nevertheless, growing design complexity exposes the limits of existing decomposition methods, which struggle to balance solution quality and runtime, and neglect edge-to-edge spacing constraints of DSA templates, compromising manufacturability. To address these issues, we introduce a new conflict graph model capturing the previously overlooked constraints and develop a local refinement strategy powered by the graph neural network, enabling efficient and scalable layout decomposition. Experiments demonstrate that our method achieves higher solution quality with faster convergence than state-of-the-art methods, while maintaining robustness across layout scales, design styles, technology nodes, and varying mask counts.
| Comments: | 9 pages. Accepted at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2026) |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.22214 [cs.AI] |
| (or arXiv:2610.22214v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.22214 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yu Feng [view email]
[v1]
Thu, 15 Oct 2026 17:00:17 UTC (5,143 KB)
来源：arXiv cs.AI · arxiv.org