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

2026-07-02 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Electronic Design Automation · Depth: Expert, medium

Summary

SINA, an open-source circuit schematic image-to-netlist generator, addresses the critical challenge of converting visual circuit schematic images from various sources into machine-readable netlists for Electronic Design Automation (EDA) tools. This fully automated pipeline 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 Integrated Circuit (IC) and Printed Circuit Board (PCB) level schematics and incorporates dedicated crossing-wires detection to differentiate wire intersections from connections. Validated using graph isomorphism techniques, SINA achieves an overall netlist generation accuracy of 96.67%, which is 2.72x higher compared to existing state-of-the-art approaches.

Key takeaway

For EDA tool developers or AI/ML engineers integrating legacy circuit design knowledge, SINA presents a robust, automated solution for converting schematic images to netlists. Its 96.67% accuracy and 2.72x improvement over current methods suggest a significant opportunity to enhance your design automation efforts. Consider adopting SINA's open-source framework or its architectural principles to accelerate design cycles and expand the utility of historical circuit data in analog and mixed-signal domains.

Key insights

SINA automates converting circuit schematic images into machine-readable netlists with high accuracy, bridging a critical gap in EDA.

Principles

• Visual circuit schematics are a vast, untapped knowledge base.
• Accurate netlist generation requires robust component and connectivity inference.
• Differentiating crossing wires from connections is crucial for fidelity.

Method

SINA's pipeline integrates deep learning for component detection, connected-component labeling for connectivity, OCR for designator extraction, and a VLM for designator assignment, including dedicated crossing-wire detection.

In practice

• Convert legacy schematic images into EDA-compatible netlists.
• Build comprehensive databases for AI-based circuit design models.
• Enable simulation and verification of designs from visual sources.

Topics

• Circuit Schematic Conversion
• Netlist Generation
• Electronic Design Automation
• Deep Learning
• Vision-Language Models
• Optical Character Recognition
• Analog/Mixed-Signal Design

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.