Multi-layer obstacle-avoiding rectilinear steiner minimal tree algorithm based on deep reinforcement learning
Year: 2025
Type: article
Abstract: As chip interconnect density increases, routing problems become increasingly complex. The routing scheme significantly impacts key performance indicators such as chip delay, power consumption, and area. In Very Large-Scale Integration (VLSI) routing, the rectilinear Steiner minimal tree is an excellent interconnect model for multi-pin nets. However, modern VLSI designs require multi-layer obstacle-avoiding routing, where wires must traverse multiple metal layers while avoiding obstacles to ensure connectivity and performance. This makes the Multi-Layer Obstacle-Avoiding Rectilinear Steiner Minimal Tree (ML-OARSMT) problem highly challenging in VLSI physical design. To address this issue, this paper proposes an ML-OARSMT construction algorithm based on deep reinforcement learning. First, a multi-layer obstacle-avoiding spanning graph is constructed by introducing vertex mapping, which connects different layers to handle the multi-layer obstacle-avoiding routing problem. Then, an agent is designed to learn edge selection for constructing the Multi-Layer Obstacle-Avoiding Steiner Tree (ML-OAST) using Double Deep Q-Network (DDQN). Finally, a post-processing stage is applied to further shorten the total wirelength through fast pruning and local optimization. Experimental results demonstrate that the proposed algorithm achieves better wirelength quality compared to state-of-the-art heuristic algorithms. Additionally, an ablation study confirms the effectiveness of DDQN in routing optimization.
Authors: Xing Huang, Yuhan Zhu, Yanbo Xu, Yajun Xie, Genggeng Liu
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,990
OpenAlex ID: W4413375471