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Enhancing Confidence and Interpretability of a CNN-Based Wafer Defect Classification Model Using Temperature Scaling

Jieun Lee1, Yeonwoo Ju1, Junho Lim1

  • 1Department of System Semiconductor Engineering, Sangmyung University, Cheonan 31066, Republic of Korea.

Micromachines
|September 27, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces an advanced wafer defect classification model that enhances accuracy, confidence, and interpretability in semiconductor manufacturing. The model achieves high accuracy while providing explainable predictions for intelligent quality management.

Area of Science:

  • Semiconductor Manufacturing
  • Artificial Intelligence
  • Quality Control

Background:

  • Accurate wafer defect classification is crucial for semiconductor yield and quality.
  • Existing methods primarily focus on classification accuracy, neglecting confidence and interpretability.

Purpose of the Study:

  • To develop a model for simultaneous assessment of accuracy, prediction confidence, and interpretability in wafer defect classification.
  • To address the class imbalance issue in defect datasets.
  • To enhance the practical applicability of AI models in semiconductor production.

Main Methods:

  • Utilized a weighted cross-entropy loss function to handle class imbalance.
  • Employed a convolutional neural network (CNN)-based model for classification.
Keywords:
convolutional neural networkdeep learningdefect analysisgradient-weighted class activation mappinglocal interpretable model-agnostic explanationssemiconductortemperature-scalingwafer map

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  • Applied temperature scaling to improve prediction confidence.
  • Integrated Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability.

    • Main Results:

    • Achieved a high classification accuracy of 97.8% on the test dataset.
    • Successfully enhanced prediction confidence using temperature scaling.
    • Provided visualized explanations for model predictions, enabling user understanding of the decision-making process.

    Conclusions:

    • The proposed model offers a comprehensive approach to wafer defect classification by integrating accuracy, confidence, and interpretability.
    • Explainable predictions enhance the trustworthiness and adoption of AI in semiconductor quality management.
    • This research paves the way for next-generation intelligent quality management systems in semiconductor manufacturing.