LLMS FOR ANALOG CIRCUIT DESIGN CONTINUUM (ACDC)
Unlocking Analog Circuit Design with LLMs: A Comprehensive Analysis
Large Language Models (LLMs) and transformer architectures show impressive reasoning and generation capabilities across diverse natural language tasks. However, their reliability and robustness in real-world engineering domains remain largely unexplored, limiting their practical utility in human-centric workflows. This work investigates the applicability and consistency of LLMs for analog circuit design—a task requiring domain-specific reasoning, adherence to physical constraints, and representations focusing on AI-assisted design where humans remain in the loop. We study how different data representations influence model behavior and compare smaller models (e.g., T5, GPT-2) with larger foundation models (e.g., Mistral-7B, GPT-oss-20B) under varying training conditions. Our results highlight key reliability challenges, including sensitivity to data format, instability in generated designs, and limited generalization to unseen circuit configurations. These findings provide early evidence on the limits and potential of LLMs as tools to enhance human capabilities in complex engineering tasks, offering insights into designing reliable, deployable foundation models for structured, real-world applications.
Executive Impact: Revolutionizing Analog Design
Our analysis reveals the transformative potential of Large Language Models (LLMs) in automating and optimizing complex analog circuit layout. By leveraging AI, enterprises can achieve significant gains in design efficiency, reduce time-to-market, and enhance reliability, pushing the boundaries of what's possible in semiconductor innovation.
0
Non-overlapping Accuracy (T5 Masking)
Yes
Mistral7B Symmetry Preservation (Synthetic v1)
Significant
Mistral7B Real-world Overlap
No
GPT-OSS-20B Real-world Overlap
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The research systematically explores the application of LLMs for analog circuit layout, progressing from simplified toy problems to complex real-world scenarios. Key steps included masking experiments to learn spatial relationships, sequential placement using Mistral7B, and joint placement with GPT-OSS-20B, along with refining data representations and parsing strategies.
Initial masking experiments with T5 achieved 87% non-overlapping accuracy on synthetic data. Fine-tuned Mistral7B successfully generated symmetric and non-overlapping layouts on synthetic data. GPT-OSS-20B demonstrated strong potential by achieving non-overlapping and near-symmetric placements on real-world netlists, indicating effective learning of spatial organization principles.
Challenges include the LLM's struggle with complex 2D spatial relationships (initially), decreasing accuracy with increased masking complexity, significant overlaps and failure to preserve symmetry on real-world netlists with Mistral7B, and the non-trivial task of reliably parsing structured output from LLMs. Data format sensitivity and limited generalization also pose issues.
Future work will focus on developing hypergraph-based representations for structural information, multimodal fine-tuning integrating topological and symbolic data, leveraging graph representations for improved transistor grouping, and exploring LLaMA 4 for multimodal tasks to enhance transformer capabilities for analog circuit design.
87%
Non-overlapping Accuracy achieved by T5 for layout generation on synthetic data.
LLM-Assisted Layout Design Workflow
Represent Netlist (text/graph)
→
Mask Components for Prediction
→
Sequential/Joint Placement Generation
→
Integrate Design Rule Checks
→
Parse LLM Output (JSON)
→
Refine Layout
| Feature/Model | GPT2 (Small) | Mistral-7B | GPT-OSS-20B |
|---|---|---|---|
| 2D Spatial Reasoning | Poor (improved with Conv. Layers) | Fair (synthetic only) | Good |
| Synthetic Data Layout Quality | N/A | Symmetry & No Overlap | Symmetry & No Overlap |
| Real-world Data Layout Quality | N/A | Significant Overlaps | No Overlap, Near Symmetric |
| Output Parsing Difficulty | N/A | Moderate | High (agentic LLM needed) |
Challenges with Real-world Analog Netlists
When applying fine-tuned Mistral-7B to real-world netlists, the model struggled significantly. While space utilization was minimized, it introduced substantial overlaps and failed to preserve symmetry, highlighting the sensitivity to data format discrepancies between synthetic training data and complex real-world circuit configurations. This underscores the need for robust data representations and adaptation.
Key Takeaway: Real-world engineering data requires sophisticated model adaptation and robust handling of practical design constraints, beyond synthetic ideals. Parsing structured outputs from LLMs remains a critical hurdle.
Controlled Asymmetry
Introduced in synthetic data v3 to better reflect real-world design constraints for GPT-OSS-20B.
Summary of Findings
This research explores LLMs for analog circuit design, a complex task requiring domain-specific reasoning and adherence to physical constraints. The study progressed from smaller models (T5, GPT-2) on toy problems to larger foundation models (Mistral-7B, GPT-OSS-20B) on synthetic and real-world netlists. Key findings highlight challenges such as data format sensitivity, instability in generated designs, and limited generalization. However, with refined data representations and larger models, significant progress was made in generating layouts that minimize space, avoid overlaps, and maintain symmetry, though output parsing remains a challenge.
Conclusion
LLMs show strong potential for enhancing human capabilities in complex engineering tasks, but require robust data representations, careful prompt engineering, and advanced parsing strategies for reliable real-world deployment. Future work involves multimodal, hypergraph-based approaches.
Calculate Your Potential AI ROI
Estimate the potential time and cost savings for your enterprise by implementing AI-driven solutions in complex design workflows.
Estimated Annual Cost Savings
$0
Equivalent Hours Reclaimed Annually
0
Your Roadmap to AI-Powered Analog Design
A phased approach ensures seamless integration and maximum impact for your organization.
Phase 01: Discovery & Strategy
Comprehensive assessment of your current design workflows, infrastructure, and specific challenges in analog circuit layout. Define key objectives, success metrics, and a tailored AI integration strategy.
Phase 02: Data Preparation & Model Training
Curate and preprocess existing netlist data, circuit schematics, and layout constraints. Develop or fine-tune specialized LLM models leveraging techniques like masking, sequential placement, and continuous coordinate prediction.
Phase 03: Pilot Program & Iteration
Implement AI-assisted design in a pilot project. Validate model outputs against DRC/LVS, refine parsing strategies, and gather feedback from layout engineers. Iterate on model performance and data representations.
Phase 04: Full-Scale Deployment & Optimization
Integrate the validated AI solution into your enterprise design tools and workflows. Provide training for design teams and establish continuous monitoring for performance, reliability, and further optimization opportunities.
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