3D-IC netlist partitioning is commonly optimized using proxy objectives, while final PPA is treated as a costly evaluation rather than an optimization signal. This proxy-driven paradigm makes it difficult to reliably translate additional PPA evaluations into better PPA outcomes. To bridge this gap, we present DOPP (D-Optimal PPA-driven partitioning selection), an approach that bridges the gap between proxies and true PPA metrics. Across eight 3D-IC designs, our framework improves PPA over Open3DBench (average relative improvements of 9.99% congestion, 7.87% routed wirelength, 7.75% WNS, 21.85% TNS, and 1.18% power). Compared with exhaustive evaluation over the full candidate set, DOPP achieves comparable best-found PPA while evaluating only a small fraction of candidates, substantially reducing evaluation cost. By parallelizing evaluations, our method delivers these gains while maintaining wall-clock runtime comparable to traditional baselines.

翻译：3D-IC网表划分通常使用代理目标进行优化，而最终PPA（功耗、性能、面积）仅被视为代价高昂的评估指标，而非优化信号。这种代理驱动范式难以将额外的PPA评估结果可靠转化为更优的PPA指标。为弥合这一差距，我们提出DOPP（D最优PPA驱动划分选择）方法，该方法在代理指标与真实PPA指标之间建立了桥梁。在八个3D-IC设计中，我们的框架相比Open3DBench实现了PPA改进（拥塞度平均相对提升9.99%，布线线长7.87%，最差负时序余量7.75%，总负时序余量21.85%，功耗1.18%）。与完整候选集上的穷举评估相比，DOPP在仅评估极小部分候选集时即可获得可比拟的最佳PPA，显著降低了评估成本。通过并行化评估，该方法在保持与传统基线相当的时间开销的同时，实现了上述性能增益。