Reinforcement Learning-Driven Window Selection for Enhanced Window-Based Rip-up and Reroute in Chip Detailed Routing

With increasingly complex design rules and pin density in advanced technology nodes, achieving a violation-free layout has become more challenging, also making rip-up and reroute (RUR) the most runtime-intensive component of detailed routing. We propose a novel reinforcement learning (RL)based approach to enhance the window-based RUR process. Our method features a dynamic window generation strategy that adjusts window size and position based on the distribution of design rule violations (DRV), enabling efficient targeting of congested areas. By leveraging the predictive capabilities of RL, our approach aims to minimize DRVs and achieve high-quality routing results. Experimental results demonstrate that our method outperforms the state-of-theart detailed routers, TritonRoute, achieving a DRV-free solution, averagely improving wirelength by 0.07%, via count by 2.42%, and consuming almost the same average runtime.