arXiv:2611.08175v1 Announce Type: new
Abstract: Automating printed circuit board (PCB) routing requires coordinating board-level connectivity decisions with geometric execution under strict design rules. Although recent agents can interact with native routing tools, reliable routing on complex boards remains challenging. We present PCBRouteAgent, a multimodal PCB routing agent inspired by how human engineers coordinate global strategy with local execution. A Global Routing Planner combines board images and structured electrical and geometric information to specify connection topology, routing order, corridors, and layer transitions. A Detailed Routing Executor realizes these guides through KiCad's native routing engine and performs bounded local recovery. Unresolved failures are returned as structured feedback, enabling targeted guide revisions while preserving accepted routes. We evaluate PCBRouteAgent on 247 synthetic and real-world boards from four PCBWorld-Bench test splits. It achieves clean-pass rates of 100%, 94.9%, 100%, and 40% on D2, D3-A, D3-B, and D3-C, respectively. On the three real-world subsets, PCBRouteAgent outperforms the best evaluated baseline in clean-pass rate by 13.9, 36, and 10 percentage points, respectively.
arxiv
PCBRouteAgent: LLM Agent for Automated PCB Routing
PCBRouteAgent is a multimodal PCB routing agent that combines a Global Routing Planner with a Detailed Routing Executor to automate printed circuit board routing. It uses board images and structured electrical/geometric data to guide KiCad's native routing engine, with bounded local recovery and structured feedback for revisions. Evaluated on 247 boards across four PCBWorld-Bench splits, it achieves clean-pass rates of 100%, 94.9%, 100%, and 40%, outperforming baselines on real-world subsets by 10-36 percentage points.
This paper presents a concrete, recent AI agent with strong benchmark results, offering practical value for electronics design automation.
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