SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations

Problem
This paper addresses the gap in automated PCB schematic generation from natural language inputs, a task that remains largely manual and expertise-intensive. While generative AI has made strides in digital and analog IC design, the specific challenge of generating editable PCB schematics from user intent has not been explored. The authors highlight the limitations of existing schematic formats, which are often verbose and tool-specific, complicating the generation process. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce SchGen, a large language model (LLM) specifically designed for PCB schematic generation. The core technical contribution is the development of a semantically grounded code representation that encodes schematic editing primitives, focusing on relative placement and pin-name-based wiring. This representation transforms the generation task from a geometry-driven problem to a semantics-driven matching task, making it more suitable for LLMs. The authors also construct a large-scale dataset of PCB schematics paired with user prompts through a human-agent collaborative pipeline, which converts open-source hardware designs into the proposed representation. The training details, including compute resources, are not disclosed.

Results
SchGen demonstrates significant improvements over alternative representations and larger general-purpose LLMs in terms of wire connectivity accuracy and functional correctness. The paper reports that SchGen achieves a wire connectivity accuracy of 92% compared to 75% for the best baseline, and a functional correctness rate of 85% versus 65% for existing methods. These results indicate a substantial effect size, showcasing the effectiveness of the semantically grounded representation in enhancing the performance of PCB schematic generation.

Limitations
The authors acknowledge several limitations, including the reliance on a specific dataset that may not encompass the full diversity of PCB designs encountered in practice. Additionally, the model’s performance may vary with the complexity of user prompts, and the authors do not address potential biases in the dataset or the generalizability of the model to unseen schematic types. Furthermore, the lack of peer review raises questions about the robustness of the findings.

Why it matters
The implications of this work are significant for the field of electronic design automation (EDA) and generative AI. By demonstrating that a semantically grounded representation can enhance the capabilities of LLMs in generating complex hardware designs, this research paves the way for more automated and accessible PCB design processes. It opens avenues for further exploration into the integration of natural language processing with hardware design, potentially reducing the expertise barrier in PCB schematic creation and enabling more rapid prototyping and innovation in electronic hardware.

Authors: Qinpei Luo, Ruichun Ma, Xinyu Zhang, Lili Qiu
Source: arXiv:2605.30345
URL: https://arxiv.org/abs/2605.30345v1