Revolutionizing Chip Design using Generative AI in Semiconductors: A Case Study Approach

The semiconductor business is a major force behind innovation in technology, and artificial intelligence (AI) has the potential to significantly increase both output and effectiveness within the sector. AI has the potential to drastically cut costs, improve the effectiveness of manufacturing, and improve product quality by decreasing faults and optimizing use of materials. Though there is growing enthusiasm for AI applications for the semiconductor sector, thorough evaluations that methodically evaluate the body of research and pinpoint the potential and problems in this area are still absent. By giving a comprehensive overview of AI-driven methods for semiconductor production optimization and providing insightful information for future research paths, this study seeks to close this knowledge gap. This study offers information about the individual AI approaches used and their influence on various phases of semiconductor production by looking at previous research and real-world examples. This study is to educate students and shine the light on work undertaken in this field by revealing the different uses of AI, pointing out current shortcomings, and offering pertinent ideas. We examine how AI is utilized to solve architectural difficulties, optimize efficiency, and drive development in chip creation via an assortment of 20 cases including top businesses like Intel, Samsung, NVIDIA, IBM, TSMC, and more.