# A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models

arXiv: https://arxiv.org/abs/2604.18806 · HTML: https://mlsanity.com/paper/2604.18806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [High-Quality Hypergraph Partitioning](https://mlsanity.com/paper/2106.08696/) (2021)
- [Open3DBench: Open-Source Benchmark for 3D-IC Backend Implementation and PPA Evaluation](https://mlsanity.com/paper/2503.12946/) (2025)

## Abstract (arXiv)

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.