With the aggressive scaling of VLSI technology, the explosion of layout patterns creates a critical bottleneck for DFM applications like OPC. Pattern clustering is essential to reduce data complexity, yet existing methods struggle with computational prohibitiveness ($O(N^2)$ comparisons), sub-optimal discrete sampling for center alignment, and difficult speed-quality trade-offs. To address these, we propose an Optimal Alignment-Driven Iterative Closed-Loop Convergence Framework. First, to resolve alignment ambiguity, we introduce a hybrid suite of high-performance algorithms: an FFT-based Phase Correlation method for cosine similarity constraints, and a Robust Geometric Min-Max strategy for edge displacement constraints that analytically solves for the global optimum. Second, we model clustering as a Set Cover Problem (SCP) using a Surprisal-Based Lazy Greedy heuristic within a coarse-to-fine iterative refinement loop to ensure convergence. Additionally, a multi-stage pruning mechanism filters over 99% of redundant computations. Experimental results on the 2025 China Postgraduate EDA Elite Challenge benchmark demonstrate a 93.4% compression ratio relative to raw inputs and an over 100x speedup compared to the official baseline, effectively handling tens of thousands of patterns in seconds. Securing First Place among 77 teams, this approach proves its superiority in solving the NP-Hard layout clustering problem with an optimal balance of scalability and precision.

翻译：随着VLSI技术的急剧微缩，版图模式数量的爆炸式增长为OPC等DFM应用带来了关键瓶颈。模式聚类对于降低数据复杂性至关重要，然而现有方法面临计算复杂度高（$O(N^2)$次比较）、中心对齐离散采样次优以及速度与质量难以权衡等问题。为解决这些挑战，本文提出了一种最优对齐驱动的迭代闭环收敛框架。首先，为解决对齐模糊性问题，我们引入了一套高性能混合算法：基于FFT的相位相关方法用于余弦相似度约束，以及一种鲁棒几何最小-最大策略用于边缘位移约束，该策略通过解析方法求解全局最优解。其次，我们将聚类建模为集合覆盖问题（SCP），在粗到细的迭代优化循环中采用基于信息熵的惰性贪婪启发式算法以确保收敛。此外，通过多级剪枝机制过滤超过99%的冗余计算。在2025年中国研究生EDA精英挑战赛基准测试上的实验结果表明，相对于原始输入数据实现了93.4%的压缩率，与官方基线相比加速超过100倍，能够在数秒内有效处理数万个版图模式。该方法在77支参赛队伍中荣获第一名，证明了其在解决NP-Hard版图聚类问题时，在可扩展性与精度之间实现了最优平衡的优越性。