VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing
cs.MA, cs.AI, cs.AR
Submitted: 2026-01-12
Updated: 2026-09-17
Comments: submitted to AI for Chip Design - NeurIPS 2026 Workshop
License: http://creativecommons.org/licenses/by/4.0/
The gist: Vision Language Models (VLMs) have demonstrated remarkable potential in multimodal reasoning.
Terminology
Abstract
Vision Language Models (VLMs) have demonstrated remarkable potential in multimodal reasoning. However, they can have spatial blindness and logical hallucinations when interpreting densely structured engineering content, such as analog circuit schematics. To address these challenges, we propose a Vision Language Model-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing (VLM-CAD) designed to support step-by-step reasoning over multimodal evidence. VLM-CAD bridges the modality gap by integrating a neuro-symbolic structural parsing module, Image2Net, which transforms raw pixels into explicit topological graphs and structured JSON representations to anchor VLM interpretation in deterministic facts. To ensure the reliability required for engineering decisions, we further propose ExTuRBO, an Explainable Trust Region Bayesian Optimization method. ExTuRBO employs agent-generated semantic seeds to warm-start local searches and uses Automatic Relevance Determination to provide sensitivity evidence for the final design report. Experimental results on 12 sizing tasks covering six circuits and four technology platforms show that VLM-CAD achieves a pooled Strict Pass@1 of 23.3% and a Relaxed Pass@1 of 91.7%, while providing sensitivity evidence for final design reports.
Sources
- AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting
- CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs
- ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
- Emerging ML-AI Techniques for Analog and RF EDA
- Qwen3 Technical Report
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