human-agent collaborative pipeline

SchGen: LLM-Based PCB Schematic Generation via Semantic Code Representations

SchGen is presented as the first large language model system capable of generating editable PCB schematics from natural-language descriptions. The approach introduces a semantically grounded code representation that replaces verbose, geometry-heavy schematic formats with relative placement and pin-name-based wiring primitives, reframing the problem as a semantics-driven matching task. A large-scale dataset was constructed via a human-agent collaborative pipeline converting open-source hardware designs into the new representation. Experiments show SchGen outperforms alternative representations and larger general-purpose LLMs on wire connectivity accuracy and functional correctness.