Presentation
PCBgen3D: A Self-Correcting Graph-Based MLLM Framework for Automated PCB 3D Models Generation
DescriptionGenerating three-dimensional (3D) models for Printed Circuit Board (PCB) components from manufacturer datasheets is a foundational yet inefficient step in Electronic Design Automation (EDA) workflows. Current processes remain heavily manual, relying on skilled engineers to interpret 2D views, textual specs, and dimension tables. This manual dependence leads to high error rates and limited scalability. To tackle these challenges, we propose PCBgen3D, a self-correcting graph-based multimodal Large Language Model (MLLM) framework. It integrates a suite of coordinated modules, each enhanced by advanced MLLMs, to automate high-precision 3D modeling. These modules follow a structured pipeline of data extraction, task planning, and process refinement, overseen by an iterative self-correction loop. A core innovation is its dynamic task graph, which decomposes complex PCB modeling into adaptive sub-tasks, and incorporates a self-correction mechanism to fix errors. Evaluated on a dataset containing components of various package types and from different manufacturers, PCBgen3D outperform state-of-the-art general-purpose MLLM on industrial-grade PCB 3D modeling tasks.
Event Type
Research Manuscript
TimeMonday, July 271:30pm - 1:42pm PDT
LocationMtg Room 101A
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