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

ArXi:2605.01404v1 Announce Type: cross 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.