AMSnet-q: Unsupervised Circuit Identification and Performance Labeling for AMS Circuits
Analog and mixed-signal (AMS) circuit design remains heavily reliant on expert knowledge. While recent AI-driven automation tools can generate candidate topologies, they critically depend on manually curated datasets with functional and performance annotations -- a requirement that current large language models (LLMs) and vision models cannot automate. Existing approaches still require domain experts to manually interpret circuit functionality. We present AMSnet-q, a fully automated, unsupervised pipeline that eliminates human-in-the-loop annotation by converting schematic images directly into a labeled AMS circuit database. Unlike prior work that stops at netlist extraction, our framewor...
| Authors | Ze Zhang, Junzhuo Zhou, Yichen Shi et al. |
|---|---|
| Primary category | cs.AR |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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