Presentation
Pcbgen: Self-Evolving LLM Agent for Specification-to-Schematic PCB Design with Parameter Tuning
DescriptionPrinted circuit board (PCB) design is increasingly critical as artificial intelligence (AI) systems demand higher integration density and stricter electrical constraints, while purely manual workflows struggle to keep up. We introduce PCBgen, a spec-to-schematic framework that integrates PCB knowledge graph (PCB-KG)-based retrieval, constraint-aware pre-filtering, a multi-modal intermediate representation (IR), and SPICE-in-the-loop, training-free Group Relative Policy Optimization (TF-GRPO) into a unified closed-loop pipeline for board-level design. This pipeline compresses an otherwise combinatorial search space (up to 10^20 candidates) to roughly 10^3 simulatable designs while preserving semantic and electrical validity. Extensive experiments for power supply designs demonstrate that, on a 336-case benchmark spanning six topology families and three design regimes, PCBgen improves topology adaptation (TA) by up to 57%, raises Pass@5 from 41% to 74%, and cuts token usage and wall-clock time by more than 50% compared with LLM-only baselines. In a 24-case comparison with human designers using existing vendor tools, it achieves 3.70x and 4.74x speedups in topology selection and schematic verification with comparable Pass@1, yielding an overall return on investment (ROI) of 4.39x and pointing toward a practical route to agentic, closed-loop PCB power supply design.
Event Type
Research Manuscript
TimeWednesday, July 294:06pm - 4:18pm PDT
LocationMtg Room 101B