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

2026-05-02

AI Summary

Analog and mixed-signal (AMS) circuit design heavily relies on expert knowledge, and existing automation approaches struggle to scale due to the need for manual annotation of circuit functionality and performance. This work proposes the first end-to-end unsupervised framework that, given only a single testbench template per circuit class, automatically extracts netlists from schematic images, generates topology-aware testbenches, and performs closed-loop functional identification and performance labeling through simulation and sizing validation. Evaluated in a 28 nm process, the method processes 739 schematic images to construct a dataset comprising four circuit categories, 105 distinct topologies, and 89,789 annotated configurations, significantly enhancing both the efficiency and objectivity of AMS dataset creation.

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.

Problem

• analog and mixed-signal circuits
• unsupervised learning
• circuit identification
• performance labeling
• automated annotation

Innovation

• unsupervised circuit identification
• automated testbench generation
• schematic-to-netlist conversion
• simulation-based validation
• AMS circuit labeling