American Journal of Advanced Multidisciplinary Research and Innovation

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Intelligent Semiconductor Manufacturing: Predictive Quality Control and Process Innovation

Author(s) Peter Adriaens
Country United States
Abstract The semiconductor industry is undergoing a major transformation driven by increasing device complexity, shrinking process geometries, heterogeneous integration, advanced packaging and growing demand for high-performance computing, artificial intelligence and edge devices. Conventional quality-control approaches that rely heavily on post-process inspection are increasingly insufficient for managing complex and tightly controlled manufacturing environments. Intelligent semiconductor manufacturing combines artificial intelligence, machine learning, advanced sensing, statistical process control, digital twins, industrial Internet of Things technologies and real-time analytics to create predictive and adaptive manufacturing systems. This paper examines the role of intelligent technologies in predictive quality control and process innovation across semiconductor fabrication and packaging. It proposes an Integrated Intelligent Semiconductor Quality Framework that connects process sensing, equipment data, wafer-level measurements, defect detection, predictive modelling, root-cause analysis and closed-loop process optimisation. Applications in wafer fabrication, lithography, deposition, etching, chemical-mechanical planarisation, metrology, inspection and advanced packaging are discussed. Particular attention is given to predictive quality models, virtual metrology, anomaly detection, predictive maintenance and AI-assisted defect classification. The paper also evaluates challenges related to data heterogeneity, concept drift, rare defects, model explainability, sensor reliability, cybersecurity and integration with legacy manufacturing execution systems. The study argues that intelligent semiconductor manufacturing should move beyond detecting defects after they occur toward anticipating quality deviations and adapting process conditions before yield is compromised. The paper concludes that the convergence of AI, semiconductor process engineering, digital twins and real-time manufacturing intelligence can enable more resilient, efficient and autonomous semiconductor production systems.
Keywords Intelligent Manufacturing, Semiconductor Manufacturing, Predictive Quality Control, Artificial Intelligence, Machine Learning, Virtual Metrology, Yield Optimisation, Defect Detection, Digital Twins, Process Innovation.
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-09
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