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Abstract
The semiconductor industry faces growing challenges in designing and optimizing complex VLSI and FPGA architectures. The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into Electronic Design Automation (EDA) offers transformative potential to enhance design accuracy, efficiency, and scalability. This paper explores the latest advancements in AI-driven methodologies for semiconductor design, with a focus on layout optimization, timing analysis, and fault tolerance mechanisms. By conducting a comprehensive literature review and presenting case studies, we highlight the contributions of AI in addressing critical challenges in VLSI and FPGA development. Experimental results reveal significant improvements in design automation processes, underscoring the importance of hybrid AI-EDA solutions in achieving optimal performance. The findings emphasize a path forward for future research and innovation in this rapidly evolving field.
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