SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence
arXiv:2607.01609v1 Announce Type: new 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 too
Advances in AI, particularly Large Language Models, are enabling the processing of complex unstructured data like circuit schematics for automated design tasks.
Automating the conversion of visual circuit schematics to machine-readable netlists can unlock vast repositories of existing design knowledge, accelerating innovation in hardware design.
The ability to automatically digitize analog and mixed-signal circuit schematics removes a significant barrier to leveraging AI for circuit design in these complex domains.
- · EDA industry
- · Semiconductor manufacturers
- · Hardware designers
- · AI/ML research labs
- · Manual schematic entry services
Reduced design cycle times and costs for analog and mixed-signal integrated circuits.
Democratization of complex circuit design by making historical knowledge more accessible and usable by AI.
Accelerated development of novel analog and mixed-signal architectures, potentially enabling entirely new classes of hardware.
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Read at arXiv cs.LG