Ai And Machine Learning–Driven Electronic Design Automation (Eda) Workflows: Transforming Semiconductor Design Through Intelligent Automation and Data-Driven Optimization

Abstract

Authors: The semiconductor industry is rapidly evolving, driven by the growing demand for high-performance computing, artificial intelligence (AI), and Internet of Things (IoT) applications. Traditional Electronic Design Automation (EDA) workflows, while effective for decades, are reaching their scalability limits due to increasing design complexity and shrinking process nodes. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into EDA has opened a new paradigm in chip design optimization, verification, and automation. This paper explores the role of AI and ML-driven EDA workflows in reshaping the semiconductor design landscape. It provides an in-depth analysis of emerging methodologies, tools, and frameworks, while addressing challenges, opportunities, and future research directions.

Keywords: AI-driven EDA, Machine Learning, Chip Design Automation, VLSI Design, Predictive Optimization, Design Verification, Semiconductor Innovation, Data-Driven Design, Automated Synthesis.