Vol. 3 No. 1 (2023): Issue 3
Articles

Fault Detection in Semiconductor Manufacturing through Naïve Bayes Classification

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  • Bianchi Luca,
  • Rossi Sofia,
  • Conti Marco

Published 2023-05-27

Keywords

  • Fault Detection,
  • Semiconductor Manufacturing,
  • Naïve Bayes Classification,
  • Manufacturing Efficiency,
  • Quality Control Systems

How to Cite

Luca, B., Sofia, R., & Marco, C. (2023). Fault Detection in Semiconductor Manufacturing through Naïve Bayes Classification. Optimizations in Applied Machine Learning, 3(1). Retrieved from https://ojs.mri-pub.com/index.php/OAML/article/view/36
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Fault detection is crucial for maintaining high-quality production in semiconductor manufacturing. Despite research advancements in fault detection methods, the complexity and variability of semiconductor manufacturing processes continue to pose challenges. Current research primarily focuses on traditional fault detection techniques, which may not effectively handle the intricacies of modern manufacturing environments. This paper addresses this gap by proposing a novel approach using Naïve Bayes classification for fault detection in semiconductor manufacturing. The study demonstrates the effectiveness of the proposed method through experiments on real-world data, highlighting its ability to accurately detect faults and improve overall manufacturing efficiency. This research contributes to the field by offering a new perspective on fault detection in semiconductor manufacturing, paving the way for more robust and reliable quality control systems in the industry.

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