Sarcouncil Journal of Multidisciplinary

Abstract: The semiconductor industry stands at a pivotal juncture as artificial intelligence transforms traditional chip design paradigms. As Moore's Law encounters fundamental physical and economic constraints, AI-driven approaches have emerged as essential enablers for continued advancement in integrated circuit development. This technical review examines how machine learning, deep learning, and reinforcement learning are revolutionizing chip design by addressing complex challenges in performance optimization, design efficiency, and manufacturing yield. Contemporary system-on-chip architectures contain billions of transistors requiring optimization across thousands of parameters while balancing competing objectives of power, performance, area, and time-to-market. AI systems demonstrate exceptional capability in navigating these multidimensional optimization problems, exploring vast design spaces to identify solutions that frequently elude human designers. The integration of AI throughout the semiconductor development pipeline represents a fundamental paradigm shift from deterministic algorithms and human intuition toward data-driven approaches that continuously learn and improve. This evolution toward intelligent design automation promises to extend innovation trajectories well beyond current limitations, enabling unprecedented levels of performance and efficiency in next-generation chips.