Ai-Assisted Physical Design Automation in VLSI Systems: Exploring Intelligent Optimization Strategies and Future Trends in Semiconductor Design Engineering

Abstract

The relentless demand for smaller, faster, and more energy-efficient integrated circuits has pushed the boundaries of Very Large-Scale Integration (VLSI) design. As the complexity of physical design in VLSI increases, traditional Electronic Design Automation (EDA) tools are struggling to meet the rising demands of performance and efficiency. Artificial Intelligence (AI), with its data-driven decision-making and learning capabilities, offers promising avenues to enhance automation in VLSI physical design. This paper delves into the emerging domain of AI-assisted physical design automation in VLSI, exploring the integration of machine learning and deep learning techniques into place and route, floorplanning, timing analysis, and power optimization. It further investigates current research, the challenges involved, and the future potential of this integration. The paper also highlights the scalability of AI approaches across different process technologies and design styles. With growing interest in explainable AI and data-efficient learning, AI is poised to revolutionize physical design workflows and reshape the future of chip design ISSN No. 2457-0095 (Online)z ISSN No. 2457-0095 (Online)z

Keywords: AI in VLSI, Physical Design Automation, Machine Learning, Deep Learning, EDA, Optimization, Chip Design, VLSI CAD