AI-Enabled Statistical Process Control for Semiconductor Manufacturing Quality Improvement
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In the highly precise and complex domain of semiconductor manufacturing, ensuring product quality and process consistency is paramount. Traditional Statistical Process Control (SPC) techniques—such as Shewhart, EWMA, and CUSUM charts—have long served as foundational tools for monitoring process stability. However, their limitations become apparent when dealing with high-dimensional, non-linear data patterns commonly encountered in modern fabrication environments. These traditional methods often rely on simplistic statistical assumptions, are reactive rather than predictive, and struggle with high false alarm rates and delayed detection of process shifts.
This research explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) into SPC frameworks to enhance defect detection, reduce false alarms, and improve overall yield. By leveraging algorithms such as Long Short-Term Memory (LSTM) networks, Autoencoders, and Random Forest classifiers, AI-enabled SPC systems can identify subtle anomalies, capture multivariate correlations, and predict process deviations with significantly higher accuracy. The paper presents a detailed methodology that includes sensor data preprocessing, model training, real-time deployment, and interpretability strategies using SHAP (SHapley Additive exPlanations).
To validate the approach, three real-world-inspired case studies from lithography, etching, and wafer deposition processes are analyzed. The AI-SPC systems demonstrated improvements in yield by up to 1.7%, reduced false alarms by over 40%, and shortened mean time to detection (MTTD) by more than 30% when compared to conventional SPC systems. The results affirm that AI-powered SPC not only augments existing process monitoring capabilities but also enables a proactive and intelligent manufacturing ecosystem.
This paper contributes to the growing body of knowledge on Industry 4.0 applications in semiconductor fabrication by demonstrating how AI can transform quality control from a retrospective tool into a predictive decision-making engine. The findings advocate for broader adoption of AI-SPC in high-precision industries to drive operational efficiency, minimize waste, and maintain competitiveness in the face of increasing process complexity.
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Copyright (c) 2025 Gaurav Rajendra Parashare
This work is licensed under a Creative Commons Attribution 4.0 International License.
