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October 3, 2025Open Access

Proactive Statistical Process Control Using AI: A Time Series Forecasting Approach for Semiconductor Manufacturing

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Authors

MSMohammad Iqbal Rasul SeeamVSVictor S. ShengTexas Tech University

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Implication

The proposed system predicts future issues in semiconductor manufacturing, enhancing quality control with machine learning.

Key Points

  • The system can forecast potential problems, reducing downtime and costs in semiconductor manufacturing.
  • Using Facebook Prophet, the model accurately predicts future measurements and classifies risk levels effectively.
  • The method integrates predictive analytics with statistical process control for proactive manufacturing quality.
  • Real data from a semiconductor company demonstrates strong prediction capabilities despite irregular measurement intervals.

Cite This Study

Seeam et al. (2025) studied this question.

synapsesocial.com/papers/68e040f3a99c246f578b36fehttps://doi.org/10.48550/arxiv.2509.16431
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Also Consider

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  3. 3Prognostics for Semiconductor Sustainability: Tool Failure Behavior Prediction in Fabrication Processes2024 · 3 citations
  4. 4Integrating Classical and Advanced SPC Tools for Preventive Quality Management2026
  5. 5Probabilistic Modeling and Machine Learning for Preventative Maintenance Prediction in Semiconductor Manufacturing2024 · 1 citations