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

2026-07-02 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Electronic Design Automation · Depth: Expert, quick

Summary

SINA is an open-source, fully automated circuit schematic image-to-netlist generator designed to convert visual representations of circuit designs from sources like research manuscripts and textbooks into machine-readable netlists. Addressing limitations in current conversion methods, which struggle with generalization across Integrated Circuit (IC) and Printed Circuit Board (PCB) schematics, component recognition, and distinguishing connected junctions from crossing wires, SINA employs a pipeline. This pipeline integrates deep learning for robust component detection, connected-component labeling for accurate connectivity inference, Optical Character Recognition (OCR) for reference designator extraction, and a Vision-Language Model (VLM) for reliable assignment. It specifically handles both IC- and PCB-level schematics and includes dedicated crossing-wires detection. Validated using graph isomorphism techniques, SINA achieves an overall netlist generation accuracy of 96.67%, which is 2.72x higher compared to prior leading approaches.

Key takeaway

Circuit design engineers or EDA tool developers integrating AI for design automation should note SINA's highly accurate, automated schematic-to-netlist conversion. You should consider adopting this open-source, multi-modal AI pipeline to efficiently process both IC and PCB schematics. This enables faster simulation, verification, and comprehensive dataset creation from visual designs, streamlining your development workflow.

Key insights

SINA automates circuit schematic image-to-netlist conversion using a multi-stage AI pipeline for enhanced accuracy.

Principles

• AI bridges visual circuit knowledge to EDA tools.
• Multi-modal AI improves image-to-netlist conversion.
• Crossing-wire detection is crucial for netlist accuracy.

Method

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

In practice

• Convert existing schematic images to netlists.
• Build AI training databases from visual designs.
• Enable simulation/verification of legacy circuits.

Topics

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

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.