Multiple patterning lithography (MPL) is regarded as one of the most promising ways of overcoming the resolution limitations of conventional optical lithography due to the delay of next-generation lithography technology. As the feature size continues to decrease, layout decomposition for multiple patterning lithography (MPLD) technology is becoming increasingly crucial for improving the manufacturability in advanced nodes. The decomposition process refers to assigning the layout features to different mask layers according to the design rules and density requirements. When the number of masks $k \geq 3$, the MPLD problems are NP-hard and thus may suffer from runtime overhead for practical designs. However, the number of layout patterns is increasing exponentially in industrial layouts, which hinders the runtime performance of MPLD models. In this research, we substitute the CPU's dance link data structure with parallel GPU matrix operations to accelerate the solution for exact cover-based MPLD algorithms. Experimental results demonstrate that our system is capable of full-scale, lightning-fast layout decomposition, which can achieve more than 10$\times$ speed-up without quality degradation compared to state-of-the-art layout decomposition methods.

翻译：多重构图光刻（MPL）被视为克服传统光学光刻分辨率限制的最有前景的技术之一，这是由于下一代光刻技术的延迟。随着特征尺寸不断减小，多重构图光刻版图分解（MPLD）技术对于提升先进节点的可制造性日益关键。分解过程指根据设计规则和密度要求将版图特征分配到不同掩模层。当掩模数量$k \geq 3$时，MPLD问题为NP难问题，因此在实际设计中可能面临运行时间开销。然而，工业版图中的布局图案数量呈指数级增长，这阻碍了MPLD模型的运行效率。在本研究中，我们采用并行的GPU矩阵运算替代CPU的舞蹈链数据结构，以加速基于精确覆盖的MPLD算法求解。实验结果表明，我们的系统能够实现全尺度、闪电般的版图分解，在无质量损失的情况下，相较于现有最先进的版图分解方法可获得超过10倍的加速比。