REVOLUTIONIZING STATIC TIMING ANALYSIS THROUGH MACHINE LEARNING INTEGRATION - Bharathi Guvvala , (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2025)
The rapid evolution of Machine Learning (ML) in Static Timing Analysis (STA) represents a transformative approach to electronic design automation. This comprehensive article explores the integration of advanced ML techniques into semiconductor design workflows, demonstrating significant improvements in timing prediction, computational efficiency, and design verification processes. By leveraging sophisticated neural network architectures and deep learning methodologies, researchers have developed innovative approaches that fundamentally reshape traditional timing analysis methodologies, offering unprecedented insights into circuit performance and optimization strategies.