Transformer-based Reinforcement Learning for Net Ordering in Detailed Routing

With feature size shrinking and design complexity increasing, detailed routing has become a crucial challenge in VLSI design. Although detailed routers have been proposed to judiciously handle hard-to-access pins and various design rules, their performances are sensitive to the order of nets to be routed, especially for those sequential routers with ripup-and-reroute scheme. In the published literature, net ordering strategies mainly rely on experts' knowledge to design heuristics to guarantee their performances. In this paper, we propose a novel transformer-based reinforcement learning framework for net ordering in detailed routing, aiming at automatically gaining failure/success routing experiences and building net order policies to guide detailed routing. Our experimental results show that our framework can effectively reduce the number of design rule violations and routing cost with comparable wirelength and via count, with comparison to state-of-the-art approaches.