AI-Driven Electronic Design Automation (EDA) for VLSI

Abstract

With the rapid advancement of semiconductor technology, the design complexity of Very Large Scale Integration (VLSI) circuits has increased dramatically. Traditional Electronic Design Automation (EDA) tools, while effective, struggle to handle the growing scale and intricacy of modern designs. Artificial Intelligence (AI) has emerged as a promising solution to address these challenges by introducing intelligent optimization, predictive analysis, and automation into the VLSI design process. This paper reviews recent trends in AI-driven EDA, highlighting key techniques, tools, and applications. We explore machine learning and deep learning models for placement, routing, timing analysis, and power optimization. Furthermore, challenges, limitations, and future directions are discussed. The integration of AI into EDA workflows shows potential for significant improvement in design efficiency, accuracy, and scalability.

KEYWORDS: VLSI, Electronic Design Automation, AI, Machine Learning, Deep Learning, Placement, Routing, Optimization