Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation

Yichen Shi; Yuzhi Liu; Zhuofu Tao; Li Huang; Yuhao Gao; Ting-Jung Lin; Lei Hel

cs.AIJul 22, 2026

Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation

Authors: Yichen Shi, Yuzhi Liu, Zhuofu Tao, Li Huang, Yuhao Gao, Ting-Jung Lin, Lei Hel

Organizations: Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China · Shanghai Jiao Tong University, Shanghai, China · BTD Technology, Ningbo, China

Abstract

A rich and recognizable component library is the cornerstone of printed circuit board (PCB) design and generation. Traditionally, engineers manually create symbols and footprints and design PCB schematics, which is time-consuming and error-prone. Leveraging multimodal large language models (MLLMs), we develop SFgen, an agentic recognition and generation flow of symbol and footprint for electronic components. SFgen achieves 86% accuracy for symbol generation and 80% accuracy for footprint generation. We use the SFgen method to create SFnet, a database of symbols and footprints. It now has 1000 components and is expanding constantly, which lays the foundation for automatic generation of PCB designs.

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SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations

Qinpei Luo, Ruichun Ma, Xinyu Zhang, Lili Qiu

University of California, San Diego · Microsoft Research Asia · The University of Texas at Austin

Printed circuit board (PCB) schematic design defines nearly all electronic hardware, but it remains manual and expertise-intensive. While generative AI has advanced digital and analog IC design, PCB schematic generation from natural-language intent is largely unexplored. This paper presents SchGen, the first large language model that generates editable PCB schematics from natural-language requests. The key challenge lies in the lack of an LLM-suited representation and a large-scale dataset. Current schematic formats are dominated by verbose, tool-specific syntax and geometry-heavy descriptions, making them difficult to generate reliably. We introduce a semantically grounded code representation that encodes schematic editing primitives with relative placement and pin-name-based wiring, transforming a geometry-driven generation problem into a semantics-driven matching task amenable to LLMs. We further construct a large-scale dataset of PCB schematics paired with user prompts via a human-agent collaborative pipeline that converts open-source hardware designs into our representation. Experiments show that SchGen significantly outperforms alternative representations and even larger general-purpose LLMs on wire connectivity accuracy and functional correctness. Our results highlight the critical role of representation design in enabling generative models for complex hardware design tasks.

Computer-Aided Design Consumer Hardware Large Language Representation Space

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Abstract

Explore similar work

SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations

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