AMSnet-q: Unsupervised Circuit Identification and Performance Labeling for AMS Circuits

• URL: http://arxiv.org/abs/2605.01404v1
• Date: Sat, 02 May 2026 12:02:17 GMT
• Title: AMSnet-q: Unsupervised Circuit Identification and Performance Labeling for AMS Circuits
• Abstract summary: We present AMSnet-q, a fully automated, unsupervised pipeline that eliminates human-in-the-loop annotation.<n>Unlike prior work that stops at netlist extraction, our framework automates the complete verification loop.<n>It processed 739 schematics from the AMSnet 1.0 dataset, automatically constructing a repository of 4 circuit classes, 105 distinct topologies, and 89,789 labeled device configurations.
• Score: 7.116571750571925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 framework automates the complete verification loop: it performs schematic-to-netlist conversion, topology-aware testbench generation, and simulation-based sizing validation to objectively determine circuit functionality. Validated in 28 nm technology, AMSnet-q processed 739 schematics from the AMSnet 1.0 dataset, automatically constructing a repository of 4 circuit classes, 105 distinct topologies, and 89,789 labeled device configurations. By decoupling human effort from dataset volume and reducing the workload to a one-time testbench template per circuit class, AMSnet-q enables scalable, objective, and fully automated AMS database construction.

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