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Wafer map failure pattern classification using geometric transformation-invariant convolutional neural network.

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  • 1Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.

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This study introduces a novel method for classifying wafer map defects, even with limited data. The technique ensures defect patterns are recognized regardless of rotation or flipping, improving semiconductor manufacturing quality.

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Area of Science:

  • Semiconductor Manufacturing
  • Artificial Intelligence
  • Computer Vision

Background:

  • Wafer map defect classification is crucial for semiconductor yield.
  • Manual defect diagnosis is challenging at scale.
  • Current deep learning methods require extensive data.

Purpose of the Study:

  • To develop a rotation- and flip-invariant method for wafer map defect classification.
  • To enable accurate classification with scarce data.
  • To improve root-cause analysis for semiconductor manufacturing.

Main Methods:

  • Utilized a convolutional neural network (CNN) backbone.
  • Incorporated Radon transformation for rotation equivariance.
  • Implemented a kernel flip module for flip invariance.
  • Applied multi-branch layer-wise relevance propagation for explainability.

Main Results:

  • Achieved class discriminant performance in scarce data scenarios.
  • Demonstrated superiority through ablation studies.
  • Validated generalization performance on augmented test sets.
  • Successfully addressed rotation and flip invariance for defect patterns.

Conclusions:

  • The proposed method effectively classifies wafer map defects with limited data.
  • Geometrical invariance is achieved through Radon transformation and kernel flipping.
  • The approach enhances defect analysis and production quality in semiconductor manufacturing.