首个面向PCB布线的多模态基准，评估大模型在真实约束下的空间推理能力
OmniRouting: A Semantic-Coupled Multimodal Benchmark for Constraint-Aware Spatial Reasoning in PCB Routing
- 构建包含1681个工业级PCB设计的多模态数据集，融合电路图与物理布局
- 涵盖几何、规则、电气功能三类任务，验证模型在实际约束下的布线能力
- 揭示当前大模型在路径规划和规则遵守上的严重不足，适合EDA与AI交叉研究者
近期大型语言模型（LLMs）在约束导航、迷宫推理和图推理方面取得显著进展，但在严格几何、拓扑和电气约束下解决复杂布线问题的能力仍基本未被探索，而布线是电子设计自动化（EDA）中最关键且最具挑战性的阶段之一。为弥合这一差距，我们提出OmniRouting，首个专为评估大模型在真实工业设计规则、可制造性和连通性约束下进行印刷电路板（PCB）布线推理而设计的大规模基准。OmniRouting包含1,681个工业级电路图耦合的PCB设计，涵盖板级几何、人工工程师布置的可布线元件位置、封装信息、焊盘位置、网表、堆叠结构及布线约束。该基准包含四项任务：(1) 几何布线推理，生成满足物理空间限制的铜迹线、过孔和层分配；(2) 设计规则感知布线推理，生成满足间距、线宽、过孔、避障和板边界约束的可布线布局；(3) 电气功能推理，在保持电路图指定连通性的同时，基于网络名称和功能角色进行正确布线；(4) 工具增强型智能体布线，利用外部工具完成前三个任务。结果表明，当前大模型在路径规划、设计规则遵循和电气功能一致性方面存在明显缺陷。我们将开源全部基准数据、评估代码和工具接口，以促进未来研究。
原文摘要 · Abstract (English)
Recent large language models (LLMs) have demonstrated remarkable progress in constraint-aware navigation, maze reasoning, and graph reasoning. However, their ability to reason about complex routing problems under strict geometric, topological, and electrical constraints remains largely unexplored, despite routing being one of the most challenging and critical stages of electronic design automation (EDA). To bridge this gap, we introduce OmniRouting, the first large-scale benchmark designed to evaluate LLMs on printed-circuit-board (PCB) routing reasoning under real-world industrial design-rule, manufacturability, and connectivity constraints. OmniRouting contains 1,681 industrial-grade schematic-coupled PCB designs, including board geometries, routable component placements by human engineers, footprints, pad locations, netlists, stackup information, and routing constraints. The benchmark comprises four tasks: (1) geometric routing reasoning, generating physically valid copper traces, vias, and layer assignments to connect circuit nets within constrained board regions; (2) design-rule-aware routing reasoning, producing routable layouts that satisfy clearance, trace-width, via, obstacle-avoidance, and board-boundary constraints; (3) electrical functionality reasoning, preserving schematic-specified connectivity while reasoning over net names and functional roles to produce electrically correct routing; and (4) tool-augmented agentic routing, leveraging external tools for tasks (1)-(3). Our results reveal substantial limitations of current LMMs in PCB routing, including weak path-planning capabilities, poor adherence to design-rule constraints, and inconsistent preservation of electrical functionality. We will open-source all benchmark data, evaluation code, and tool interfaces to facilitate future research.
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