Lightweight AI tool analyzes analog circuit layouts with conversation
Inspector: Conversational and Lightweight Analyzer of Analog Circuit Layouts Using LLM and CNNs
Machine LearningComputer Vision and Pattern Recognition
Summary
Analyzing the detailed physical designs of analog circuits is complicated but essential for ensuring circuits work well. The authors developed a new tool that uses two types of artificial intelligence—language models and image-recognition models—to understand and analyze these complex circuit designs directly from industry-standard files. This tool can chat with designers, making the interaction easier and more intuitive. Tests on thousands of circuit designs show it performs much better than existing general AI models while being more lightweight.
What this means in practice
- •For analog circuit designers: Interact directly with circuit layout files using conversational AI to quickly analyze design issues and physical layout characteristics.
- •For hardware verification teams: Accelerate verification processes by automatically extracting key layout information from GDSII files with improved accuracy and efficiency.
Authors
Abril Cano Castro, Giuseppe Chiari, Michele Piccoli, Federico Viola, Davide Zoni
Abstract
The integration of artificial intelligence into computer-aided design frameworks has sparked a shift in the design of analog integrated circuits (ICs), transitioning the field from using manual and algorithmic-based solutions to adopting automated and intelligent paradigms. In this scenario, the GDSII file represents the industry-standard database containing the ultimate and most accurate source of information of the analog circuit, encapsulating the complex physical geometries and parasitic realities that define tape out performance. This paper proposes a novel framework that combines fine-tuned LLMs and CNNs to analyze GDSII files of analog circuits, enabling a conversational interface between the tool and the designers. Experimental results using thousands of analog designs across four realistic tasks demonstrate that the proposed solution outperforms state-of-the-art general-purpose massive VLMs by a significant margin (up to 81%), thus providing a lightweight solution to the problem of GDSII analysis.