MGARoute: Efficient Analog Routing With Multi-Stage Rip-Up and Rerouting Under Geometric and Symmetry Constraints

Routing is a critical path-planning challenge in integrated circuit (IC) design, especially in analog IC design, where the complexity of the routing problem increases significantly owing to complex constraints and process parameters. Traditional automated routing techniques often rely on manual experience, limiting the effectiveness of the algorithms. To address this limitation, this study proposes a multilevel fast detour and rerouting algorithm based on reinforcement learning that integrates multiple symmetry and geometric constraints. The algorithm can effectively improve the design efficiency and reduce human error while optimizing the circuit performance and power consumption. The results show that the framework significantly improves the routing speed, reduces the number of vias, and is essentially free of DRC reporting errors on a variety of circuits in SMIC’s 180 nm technology. In addition, the algorithm roughly agrees with the manual routing results in terms of simulation performance and maintains good performance.