RL-Ripper:

Physical designers have been using heuristics to solve challenging problems in routing. However, these heuristic solutions are not adaptable to the ever-changing fabrication demands and their effectiveness is limited by the experience and creativity of the designer. Reinforcement learning is an effective method to tackle sequential optimization problems due to its ability to adapt and learn through trial and error, creating policies that can handle complex tasks. This study presents an RL framework for global routing that incorporates a self-learning model called RL-Ripper. The primary function of RL-Ripper is to identify the best nets to rip to decrease the number of total short violations. In this work, the final global routing results are evaluated against CUGR, a state-of-the-art global router, using the ISPD 2018 benchmarks. The proposed RL-Ripper framework's approach can reduce the short violations compared to CUGR. Moreover, the RL-Ripper reduced the total number of short violations after the first iteration of detailed routing over the baseline while being on par with the wirelength, VIA, and runtime. The major impact of the proposed framework is to provide a novel learning-based approach to global routing that can be replicated for newer technologies.