🤖 AI Summary
This study systematically evaluates the human-AI collaboration suitability of large language models (LLMs) in analog and custom digital circuit design (ACDC), focusing on their reliability and robustness in domain-specific reasoning, adherence to physical constraints, and structured representation tasks. Through controlled comparative experiments, we assess models—including T5, GPT-2, Mistral-7B, and GPT-oss-20B—on multimodal circuit data: netlists, natural-language descriptions, and symbolic constraints. Results show that smaller models exhibit greater robustness on constrained subtasks, whereas larger models suffer from poor generalization and frequent violations of fundamental physical laws. We introduce, for the first time, an engineering-deployment-oriented reliability evaluation framework that identifies critical limitations—including data representation sensitivity and design inconsistency—under real-world ACDC conditions. This work establishes a methodological foundation and practical guidelines for AI-assisted, high-reliability circuit design.
📝 Abstract
Large Language Models (LLMs) and transformer architectures have shown 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. In this work, we investigate the applicability and consistency of LLMs for analog circuit design -- a task requiring domain-specific reasoning, adherence to physical constraints, and structured 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.