TopoBalance: Diverse Legal Layout Generation via Support Expansion and Distribution Balancing

Generating large, diverse, and distributionally balanced libraries of legal layout patterns is important for design-for-manufacturability applications, yet remains challenging with limited legal training data. The combinatorial topology space leads to sparse coverage of complexity labels, while topology filtering and physical-feasibility constraints introduce acceptance-induced distribution shifts that concentrate the final legal library on a limited subset of labels. To address these challenges, we propose TopoBalance, a distribution-aware layout-pattern generation framework that integrates training-stage support expansion and generation-stage distribution balancing. TopoBalance employs Topology-Valid RegSLERP (TV-RegSLERP) for training-stage support expansion and Distribution-Balanced Adaptive Sampling and Generation (DBAS-Gen) for closed-loop distribution balancing during generation. Experiments on ICCAD 2016 show that TopoBalance substantially improves complexity-label diversity and distributional balance while maintaining a high legality. It markedly reduces concentration on dominant complexity regions, yielding a broader and more balanced legal-layout library. The code and datasets are available at https://anonymous.4open.science/r/RegSLERP-Hybrid-5BB1/.