Multi-Agent Yield Analysis for Circuit Design

Semiconductor yield estimation presents a critical challenge in modern manufacturing, directly impacting production costs and market competitiveness. Traditional estimation methods, particularly Monte Carlo simulation, while reliable, become computationally prohibitive for complex modern circuits. Contemporary approaches, including importance sampling and machine learning techniques, face fundamental limitations in consistency across circuit topologies and practical validation. This work introduces YieldAgent, a novel Large Language Model (LLM)-powered framework that revolutionizes yield estimation through dynamic integration of multiple analytical strategies. YieldAgent employs a three-layer agent architecture to analyze circuit characteristics and historical data, optimizing estimation methods while balancing computational efficiency and precision. The framework incorporates Retrieval-Augmented Generation for domain knowledge integration and Tree-structured Parzen Estimators for dynamic hyperparameter optimization. Experimental validation across 12nm and 40nm technology nodes demonstrates that YieldAgent reduces computational overhead by up to 2.9 × while maintaining or exceeding state-of-the-art accuracy. The system's ability to adapt across different circuit topologies and technology nodes establishes a new paradigm for scalable, intelligent yield estimation in electronic design automation.