Placement Tomography-Based Routing Blockage Generation for DRV Hotspot Mitigation

A fundamental goal in modern physical design is for the post-route layout to have a fixable number of remaining design rule violations (DRVs). We study how to apply routing blockages to a fixed placement solution, so as to "condition" the routing problem and minimize DRVs in the post-route outcome. Motivated by the widening turnaround time gap between early global routing (eGR) and detailed routing, we propose placement tomography (that uses multiple views of a placement from near-free eGR runs) as a new basis for generating layer-wise route blockages and mitigating post-route DRVs. Our framework includes (i) DRVNet, a machine learning model that predicts layer-wise DRV hotspots; (ii) BlkgComp, a learning-based model for assessing the relative effectiveness of two different routing blockages in mitigating DRVs in hotspots; and (iii) a reinforcement learning approach with BlkgComp to generate routing blockages for the hotspots predicted by DRVNet. Experimental studies confirm that our BlkgComp model achieves up to 73% accuracy and 0.53 Kendall rank on the testing dataset for open-source and commercial enablements. Our framework produces routing blockage solutions that reduce post-route DRVs by up to 88% compared to baseline commercial tool flows and up to 21% compared to a human expert baseline that was able to access detailed route outcomes.