Recognition: unknown
AMSnet-q: Unsupervised Circuit Identification and Performance Labeling for AMS Circuits
Pith reviewed 2026-05-09 13:41 UTC · model grok-4.3
The pith
AMSnet-q is an unsupervised pipeline that automates schematic-to-netlist conversion, topology-aware testbench creation, and simulation-based validation to build labeled AMS circuit datasets from images.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
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.
Load-bearing premise
That automatically generated testbenches and simulation results can reliably and objectively classify circuit functionality and performance without missing edge cases or requiring per-topology human corrections beyond the initial template.
read the original 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.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents AMSnet-q, a fully automated unsupervised pipeline that converts schematic images of analog and mixed-signal (AMS) circuits into a labeled database. It automates schematic-to-netlist conversion, topology-aware testbench generation, and simulation-based sizing validation to determine functionality and performance labels. Applied to 739 schematics from the AMSnet 1.0 dataset in 28 nm technology, the pipeline produces 4 circuit classes, 105 topologies, and 89,789 labeled device configurations, with human effort reduced to a one-time testbench template per circuit class.
Significance. If the labeling process is shown to be accurate and complete, the work would be significant for enabling scalable, objective dataset construction for AI-driven AMS design automation. Current approaches rely on manual expert annotation, which this engineering pipeline aims to decouple from dataset volume, potentially accelerating research in topology generation and verification tools.
major comments (2)
- [Abstract] Abstract: The central claim that the pipeline 'objectively determine[s] circuit functionality' via simulation-based validation rests on the unverified assumption that one-time per-class testbench templates comprehensively cover all operating regimes, failure modes, and edge cases (e.g., corner-case stability or parasitic effects). No quantitative error rates, failure mode analysis, or comparison against manual labels on any subset are reported, leaving the objectivity and reliability of the 89,789 labels unverified.
- [Abstract] Abstract and results description: The reported processing of 739 schematics into 89,789 configurations with 105 topologies is presented as validation of the framework, but without any independent verification (e.g., precision/recall on a held-out manually labeled set or breakdown of misclassifications), the claim that human effort is limited to one template per class while achieving objective labeling cannot be assessed.
minor comments (1)
- [Abstract] Abstract: The phrase 'simulation-based sizing validation' is introduced without a brief definition or reference to the specific pass/fail criteria or simulation setup used, which would aid clarity on how functionality is objectively determined.
discussion (0)
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