Multipatterning is an essential decomposition strategy in electronic design automation (EDA) that overcomes lithographic limitations when printing dense circuit layouts. Although heuristic-based backtracking and SAT solvers can address these challenges, they often struggle to simultaneously handle both complex constraints and secondary objectives. In this study, we present a hybrid workflow that casts multipatterning as a variant of a constrained graph coloring problem with the primary objective of minimizing feature violations and a secondary objective of balancing the number of features on each mask. Our pipeline integrates two main components: (1) A GNN-based agent, trained in an unsupervised manner to generate initial color predictions, which are refined by (2) refinement strategies (a GNN-based heuristic and simulated annealing) that together enhance solution quality and balance. Experimental evaluation in both proprietary data sets and publicly available open source layouts demonstrate complete conflict-free decomposition and consistent color balancing. The proposed framework provides a reproducible, data-efficient and deployable baseline for scalable layout decomposition in EDA workflows.

翻译：多图案化是电子设计自动化（EDA）中的一种关键分解策略，用于克服在打印高密度电路版图时的光刻限制。尽管基于启发式的回溯算法和SAT求解器能够应对这些挑战，但它们通常难以同时处理复杂约束和次要优化目标。在本研究中，我们提出了一种混合工作流程，将多图案化问题建模为约束图着色问题的一种变体，其主要目标是最小化特征违规，次要目标是在每个掩模上平衡特征数量。我们的流程整合了两个核心组件：（1）基于图神经网络（GNN）的智能体，以无监督方式训练以生成初始着色预测；（2）优化策略（基于GNN的启发式算法和模拟退火），共同提升解的质量与平衡性。在专有数据集和公开可用的开源版图上进行的实验评估表明，该方法实现了完全无冲突的分解和一致的色彩平衡。所提出的框架为EDA工作流程中的可扩展版图分解提供了一个可复现、数据高效且易于部署的基准。