ISSN (Online): 2321-3418
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Engineering and Computer Science
Open Access

AI-Enabled Statistical Process Control for Semiconductor Manufacturing Quality Improvement

DOI: 10.18535/ijsrm/v13i06.ec07· Pages: 2279-2300· Vol. 13, No. 06, (2025)· Published: June 15, 2025
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Abstract

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.

 

Keywords

AI-SPCSemiconductor ManufacturingAnomaly DetectionProcess Shift PredictionMachine

References

  1. Okuyelu, O., & Adaji, O. (2024). AI-driven real-time quality monitoring and process optimization for enhanced manufacturing performance. J. Adv. Math. Comput. Sci, 39(4), 81-89.Google Scholar ↗
  2. Podder, I., Fischl, T., & Bub, U. (2023, March). Artificial intelligence applications for MEMS-based sensors and manufacturing process optimization. In Telecom (Vol. 4, No. 1, pp. 165-197). MDPI.Google Scholar ↗
  3. Cai, Y. Machine Learning for Anomaly Detection in Lithography Machines.Google Scholar ↗
  4. Dehaerne, E., Dey, B., Blanco, V., & Davis, J. (2024). Electron Microscopy-based Automatic Defect Inspection for Semiconductor Manufacturing: A Systematic Review. arXiv preprint arXiv:2409.06833.Google Scholar ↗
  5. Ebadi, M., Chenouri, S., Lin, D. K., & H. Steiner, S. (2022). Statistical monitoring of the covariance matrix in multivariate processes: A literature review. Journal of Quality Technology, 54(3), 269-289.Google Scholar ↗
  6. Kogileru, S., McBride, M., Bi, Y., & Ng, K. Y. (2025). Design and Development of a Robust Tolerance Optimisation Framework for Automated Optical Inspection in Semiconductor Manufacturing. arXiv preprint arXiv:2505.03576.Google Scholar ↗
  7. Li, Y., Du, J., & Jiang, W. (2024). Reinforcement learning for process control with application in semiconductor manufacturing. IISE Transactions, 56(6), 585-599.Google Scholar ↗
  8. Qiu, P. (2013). Introduction to statistical process control. CRC press.Google Scholar ↗
  9. Winkler, G., Rothe, T., Sayyed, M. A., Jäckel, L., Langer, J., Kuhn, H., & Stoll, M. (2025). Machine Learning in Chemical-Mechanical Planarization: A Comprehensive Review of Trends, Applications, and Challenges. Authorea Preprints.Google Scholar ↗
  10. Tao, F., Zhang, L., Venkatesh, V. C., Luo, Y., & Cheng, Y. (2011). Cloud manufacturing: a computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(10), 1969-1976.Google Scholar ↗
  11. Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Hengel, A. V. D. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1705-1714).Google Scholar ↗
  12. Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. (2018, July). Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 387-395).Google Scholar ↗
  13. Cai, Y. Machine Learning for Anomaly Detection in Lithography Machines.Google Scholar ↗
  14. Fan, S. K. S., Hsu, C. Y., Jen, C. H., Chen, K. L., & Juan, L. T. (2020). Defective wafer detection using a denoising autoencoder for semiconductor manufacturing processes. Advanced Engineering Informatics, 46, 101166.Google Scholar ↗
  15. Kim, D., Kang, P., Cho, S., Lee, H. J., & Doh, S. (2012). Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Systems with Applications, 39(4), 4075-4083.Google Scholar ↗
  16. Sarkar, B., & Paul, R. K. (2025). AI for Advanced Manufacturing and Industrial Applications.Google Scholar ↗
  17. Vermesan, O., Coppola, M., Bahr, R., Bellmann, R. O., Martinsen, J. E., Kristoffersen, A., ... & Lindberg, D. (2022). An intelligent real-time edge processing maintenance system for industrial manufacturing, control, and diagnostic. Frontiers in Chemical Engineering, 4, 900096.Google Scholar ↗
  18. Fan, H., Yao, P., & Chen, H. (2023, April). Application of AI-enabled Simulation in Power Package Development. In 2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) (pp. 1-5). IEEE.Google Scholar ↗
  19. Wan, J., Li, X., Dai, H. N., Kusiak, A., Martinez-Garcia, M., & Li, D. (2020). Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges. Proceedings of the IEEE, 109(4), 377-398.Google Scholar ↗
  20. Aravind, R., & Shah, C. V. (2024). Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI. International Journal Of Engineering And Computer Science, 13(01).Google Scholar ↗
  21. Markatos, N. G., & Mousavi, A. (2023). Manufacturing quality assessment in the industry 4.0 era: a review. Total Quality Management & Business Excellence, 34(13-14), 1655-1681.Google Scholar ↗
  22. Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), 43-53.Google Scholar ↗
  23. Zdravković, M., Panetto, H., & Weichhart, G. (2022). AI-enabled enterprise information systems for manufacturing. Enterprise Information Systems, 16(4), 668-720.Google Scholar ↗
  24. Bu, L., Zhang, Y., Liu, H., Yuan, X., Guo, J., & Han, S. (2021). An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform. Advanced Engineering Informatics, 50, 101370.Google Scholar ↗
  25. Ye, J., El Desouky, A., & Elwany, A. (2024). On the applications of additive manufacturing in semiconductor manufacturing equipment. Journal of Manufacturing Processes, 124, 1065-1079.Google Scholar ↗
  26. Ghosh, S. (2025). Developing artificial intelligence (AI) capabilities for data-driven business model innovation: Roles of organizational adaptability and leadership. Journal of Engineering and Technology Management, 75, 101851.Google Scholar ↗
  27. Sen, S., Husom, E. J., Goknil, A., Tverdal, S., Nguyen, P., & Mancisidor, I. (2022). Taming data quality in AI-enabled industrial internet of things. IEEE Software, 39(6), 35-42.Google Scholar ↗
  28. da Silva Ferreira, M. V., Ahmed, M. W., Oliveira, M., Sarang, S., Ramsay, S., Liu, X., ... & Kamruzzaman, M. (2024). AI-Enabled Optical Sensing for Smart and Precision Food Drying: Techniques, Applications and Future Directions. Food Engineering Reviews, 1-29.Google Scholar ↗
  29. Gao, R. X., Krüger, J., Merklein, M., Möhring, H. C., & Váncza, J. (2024). Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions. CIRP Annals.Google Scholar ↗
  30. Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics. Sensors, 21(19), 6340.Google Scholar ↗
Author details
Gaurav Rajendra Parashare
ASQ CMQ OE Master of Science in Industrial and Systems Engineering, Bachelor Production Engineering
✉ Corresponding Author
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