Extending ML-OARSMT to net open locator with efficient and effective boolean operations
Bing-Hui Jiang, Hung-Ming Chen · 2018
Multi-layer obstacle-avoiding rectilinear Steiner minimal tree (ML-OARSMT) problem has been extensively studied in recent years. In this work, we consider a variant of ML-OARSMT problem and extend the applicability to the net open location finder. Since ECO or router limitations may cause the open nets, we come up with a framework to detect and reconnect existing nets to resolve the net opens. Different from prior connection graph based approach, we propose a technique by applying efficient Boolean operations to repair net opens. Our method has good quality and scalability and is highly parallelizable. Compared with the results of ICCAD-2017 contest, we show that our proposed algorithm can achieve the smallest cost with 4.81 speedup in average than the top-3 winners.