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

Defect Classification in Wafer Inspection through k-Nearest Neighbors

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  • Wei Li,
  • Min Zhang,
  • Fang Chen

Published 2023-10-09

Keywords

  • Wafer Inspection,
  • Defect Classification,
  • k-Nearest Neighbors,
  • Accuracy Improvement,
  • Semiconductor Manufacturing

How to Cite

Li, W., Zhang, M., & Chen, F. (2023). Defect Classification in Wafer Inspection through k-Nearest Neighbors. Optimizations in Applied Machine Learning, 3(1). Retrieved from https://ojs.mri-pub.com/index.php/OAML/article/view/33
Creative Commons License

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

Abstract

In semiconductor manufacturing, wafer inspection plays a critical role in ensuring product quality and yield rate. The increasing demand for higher precision and efficiency in defect classification has driven the need for advanced inspection techniques. Current research on defect classification in wafer inspection faces challenges such as limited accuracy and efficiency. This paper presents a novel approach utilizing the k-Nearest Neighbors algorithm for defect classification, aiming to improve the accuracy and efficiency of the classification process. By analyzing the characteristics of defects on the wafer surface and optimizing the k-NN parameters, this study demonstrates innovative methods to enhance the performance of defect classification in wafer inspection, contributing to the advancement of semiconductor manufacturing quality control.

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