AI-powered semiconductor wafer fabrication: A manufacturing paradigm shift

Abstract

Artificial intelligence (AI) is reshaping the landscape of modern manufacturing by enabling intelligent, adaptive, and increasingly autonomous production systems. In high-end manufacturing processes, persistent challenges such as complex machine-tool dynamics, variability in equipment performance, and environmental instabilities hinder consistent quality and high efficiency. These challenges are further amplified in advanced manufacturing domains, particularly in semiconductor wafer fabrication, which demands ultra-high precision to be attained. AI offers powerful solutions to these challenges through its capability in real-time decision making, data driving modelling, and predictive analysis of manufacturing processes. By learning from vast datasets generated during manufacturing, AI systems can identify working patterns, optimize process parameters, detect anomalies, and guide autonomous control strategies. These advantages position AI as a key enabler for enhancing process understanding, ensuring quality assurance, and accelerating innovation in fabrication technologies. Semiconductor wafer fabrication is the cornerstone of modern electronics manufacturing, serving as the foundation process for integrated circuits production. This article reviews the transformative role of AI in wafer fabrication, highlighting its principles, applications, and prospects. Key AI technologies such as deep learning, reinforcement learning, and generative algorithms that have been widely adopted in wafer fabrication are surveyed. Special attention is given to their deployment across critical fabrication procedures including crystal growth, ingot slicing and ultraprecision machining. The review further presents state-of-the-art research and industrial implementations of AI in these domains, showcasing how intelligent models can be employed in quality prediction, regime classification, process optimization, and system control. Finally, emerging trends and future perspectives aimed at fostering more practical, deeply integrated, and robust AI applications across the manufacturing ecosystem are discussed, paving the way toward fully autonomous and self-optimizing future manufacturing processes.