AI-DRIVEN VLSI DESIGN AND AUTOMATION

The rapid increase in the complexity of Very Large-Scale Integration (VLSI) systems, along with continuous semiconductor scaling, has made conventional design and automation techniques increasingly time-consuming and resource-intensive. AI-driven VLSI design and automation have emerged as a powerful paradigm that applies machine learning, deep learning, and data-driven optimization to improve the efficiency, accuracy, and scalability of Electronic Design Automation (EDA) tools. By learning from large volumes of design data, AI techniques enable intelligent decision-making across key stages of the VLSI design flow, including architecture exploration, logic synthesis, placement and routing, timing analysis, power optimization, and verification. This chapter provides an overview of AI-based methodologies used in modern VLSI automation, emphasizing predictive modeling and reinforcement learning for faster design cycles and improved performance, power, and area (PPA) metrics. It also discusses challenges such as data quality, model interpretability, and tool integration, highlighting AI’s critical role in next-generation VLSI systems.