ICLAgent: Integrated Circuit Footprint Geometry Labeling via LMM-empowered Multi-Agent Framework

Integrated circuit (IC) footprint geometry labeling refers tothe process of converting pin diagrams in IC datasheets into machine-readable geometric parameters. This task is critical in printed circuitboard (PCB) design and component assembly, as accurate labeling en-sures proper IC placement and reliable connectivity. The process is chal-lenged by unstructured annotations, complex footprint arrangements,and abstract geometric diagrams, making fully automated labeling meth-ods inadequate. Traditional EDA tools require heavy manual input. Ex-isting automation methods, such as OCR or object detection, fail tocapture the implicit geometric relationships in IC diagrams, leaving thelabeling task incomplete. Recent work has shown that end-to-end largemultimodal models (LMMs) can perform IC geometry labeling. How-ever, by treating the task as a black box, it is prone to shortcut learningand lack interpretability. In this work, we introduce ICLAgent, thefirst multi-agent framework for fully automated IC footprint geometrylabeling that explicitly models the workflow of expert engineers to pro-duce more interpretable and reliable labeling outcomes. Furthermore, wepresent ICAgent-Instruct, the first dynamic planning and reasoningdataset tailored for IC footprint geometry labeling. Extensive experi-ments show that ICLAgent improves overall accuracy by 10.3% com-pared to the previous SOTA method and by 79.5% compared to man-ual annotation. Despite using only simple supervised fine-tuning on a7B model (Qwen2-VL-7B), ICLAgent surpasses general-purpose LMMssuch as GPT-5 (by 94.6%) and Gemini-3 Pro (by 15.2%).