R2G is a multi-view circuit graph benchmark showing that representation choice affects GNN accuracy more than model architecture, with node-centric views and deeper decoders performing best.
arXiv preprint arXiv:2503.00205
2 Pith papers cite this work. Polarity classification is still indexing.
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AnalogMaster applies large language models to end-to-end analog IC design automation, converting images to netlists and optimizing parameters to achieve 92.9% Pass@1 and 99.9% Pass@5 success on 15 test circuits using GPT-5.
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R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII
R2G is a multi-view circuit graph benchmark showing that representation choice affects GNN accuracy more than model architecture, with node-centric views and deeper decoders performing best.
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AnalogMaster: Large Language Model-based Automated Analog IC Design Framework from Image to Layout
AnalogMaster applies large language models to end-to-end analog IC design automation, converting images to netlists and optimizing parameters to achieve 92.9% Pass@1 and 99.9% Pass@5 success on 15 test circuits using GPT-5.