SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence

2026-07-01

AI Summary

Existing methods for converting circuit schematic images into EDA-processable netlists suffer from poor generalizability across IC and PCB domains, inaccurate component recognition, ambiguous connectivity inference, and frequent misinterpretation of crossing wires. This work proposes the first fully automated parsing pipeline that integrates deep learning, connected-component labeling, OCR, and vision-language models to precisely extract components, labels, and topological connections. A dedicated crossing-wire detection module is introduced to distinguish genuine electrical junctions from mere graphical overlaps. The approach unifies processing for both IC and PCB schematics and achieves a netlist accuracy of 96.67% on real-world datasets—2.72 times higher than the current state-of-the-art—while ensuring functional equivalence through graph isomorphism verification.

Abstract

Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks. However, their application to analog and mixed-signal domains remains limited by the lack of machine-readable representations of existing circuit design knowledge. Circuit schematic images found in research manuscripts, textbooks, and websites constitute a vast repository of validated designs; however, these visual representations cannot be directly processed by EDA tools. Converting them into machine-readable netlists is essential for enabling simulation, verification, and building comprehensive databases for AI-based models. Current conversion methods lack generalization across both Integrated Circuit (IC) and Printed Circuit Board (PCB) level schematics. Moreover, they struggle with component recognition and connectivity inference, and fail to distinguish between connected junctions and crossing wires. In this paper, we propose SINA, an open-source circuit schematic image-to-netlist generator. SINA is a fully automated pipeline that integrates deep learning for robust component detection, connected-component labeling for accurate connectivity inference, Optical Character Recognition (OCR) for component reference designator extraction, and a Vision-Language Model (VLM) for reliable reference designator assignment. SINA handles both IC- and PCB-level schematics and incorporates dedicated crossing-wires detection to differentiate wire intersections from connections. We validate the correctness of the generated netlists using graph isomorphism techniques. Our experiments demonstrate an overall netlist generation accuracy of 96.67%, which is 2.72x higher compared to state-of-the-art approaches.

Problem

• schematic-to-netlist
• circuit recognition
• connectivity inference
• crossing wires detection
• machine-readable representation

Innovation

• schematic-to-netlist
• deep learning
• Vision-Language Model
• crossing-wires detection
• Electronic Design Automation