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A hybrid system for detecting semiconductor wafer defects using modified MobileNet with multi-head attention.

Sharith Dhar1, Fahmid Al Farid2, Md Saiful Islam1

  • 1Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh.

Plos One
|April 8, 2026
PubMed
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This study introduces an enhanced MobileNet system with an error-correcting output code (ECOC)-based support vector machine (SVM) classifier to accurately identify defective semiconductor wafers, improving defect detection accuracy.

Area of Science:

  • Semiconductor Manufacturing
  • Computer Vision
  • Machine Learning

Background:

  • Semiconductor wafer defect identification is complex, challenging current systems with intricate patterns and long-range defect dependencies.
  • Existing methods struggle to detect small defects and handle imbalanced datasets common in wafer manufacturing.

Purpose of the Study:

  • To develop a hybrid system for improved semiconductor wafer defect detection.
  • To address limitations in capturing complex defect patterns, long-range dependencies, and minor defects.
  • To overcome class imbalance issues in defect datasets.

Main Methods:

  • A modified MobileNet architecture utilizing Swish activation and multi-head attention for feature extraction.
  • Integration of an error-correcting output code (ECOC)-based support vector machine (SVM) classifier to handle dataset imbalance.

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  • Application of histogram equalization to enhance the visibility of minor defects in wafer images.

    • Main Results:

    • The proposed hybrid system achieved a testing accuracy of 98.55% on the WM-811K dataset.
    • Superior performance metrics including AUC (99.74%), recall (93.34%), precision (95.64%), and F1-score (94.42%) were obtained.
    • Ablation studies confirmed the effectiveness of each proposed modification.

    Conclusions:

    • The hybrid MobileNet-ECOC-SVM system significantly enhances semiconductor wafer defect identification accuracy and robustness.
    • The modifications effectively address challenges like complex patterns, long-range dependencies, minor defects, and class imbalance.
    • This approach offers a promising solution for quality control in semiconductor manufacturing.