SINA converts circuit schematic images to netlists at 96.67% accuracy using deep learning, OCR, connected-component labeling, and a vision-language model, claimed 2.72x better than prior methods.
Analogcoder: Analog circuit design via training-free code generation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
AnalogRetriever maps schematics, descriptions, and netlists of analog circuits into one embedding space and achieves 75.2% average Recall@1 across six retrieval directions while boosting an agentic design framework.
Frontier-Eng is a new benchmark for generative optimization in engineering where agents iteratively improve designs under fixed interaction budgets using executable verifiers, with top models like GPT 5.4 showing limited success.
citing papers explorer
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SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence
SINA converts circuit schematic images to netlists at 96.67% accuracy using deep learning, OCR, connected-component labeling, and a vision-language model, claimed 2.72x better than prior methods.
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AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval
AnalogRetriever maps schematics, descriptions, and netlists of analog circuits into one embedding space and achieves 75.2% average Recall@1 across six retrieval directions while boosting an agentic design framework.
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Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization
Frontier-Eng is a new benchmark for generative optimization in engineering where agents iteratively improve designs under fixed interaction budgets using executable verifiers, with top models like GPT 5.4 showing limited success.