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Published as a conference paper at NeurIPS 2026

TopoScope: A Graph-Scoped LLM Agent for Printed Circuit Board Schematic Design

Jiale Guo, Shiji Tong, Weihao Li, Yu Wu, Boxian Lin, Mengji Shi

Abstract

Printed circuit boards (PCBs) are the hardware foundation of modern electronic systems. However, automating PCB schematic design remains difficult. A valid schematic must respect real-IC pin-level constraints and cross-component topological relations, neither of which is captured by generic simulation, local rule checks, or direct LLM generation. To address this challenge, we present TopoScope, a training-free LLM agent that uses the schematic graph not only as the final output, but also to guide generation and verification at runtime. TopoScope couples two mechanisms: Topology-Guided Context Scoping, which computes a scoped view for each component-level wiring decision, and Pattern-Based Rule Verification, which expresses datasheet-derived electrical constraints as graph patterns and uses them as in-loop feedback. We also introduce a 20-design benchmark spanning three complexity tiers and six application domains, with functional assertions evaluated on the generated schematic graph and calibrated against expert judgments. Across seven recent LLMs, TopoScope improves functional correctness, reduces invalid graph references, and enables more reliable generation of medium and hard multi-IC schematics compared with knowledge-provided baselines.

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