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Elevating Wafer Defect Inspection with Denoising Diffusion Probabilistic Model

Author

Listed:
  • Ping-Hung Wu

    (Product Testing Service Office, Nanya Technology Corporation, New Taipei City 243089, Taiwan)

  • Thi Phuong Hoang

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Yen-Ting Chou

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Andres Philip Mayol

    (Manufacturing Engineering and Management Department, De La Salle University, Manila 0922, Philippines
    Center for Engineering and Sustainable Development Research, De La Salle University, Manila 0922, Philippines)

  • Yu-Wei Lai

    (Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Chih-Hsiang Kang

    (Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Yu-Cheng Chan

    (Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Siou-Zih Lin

    (AI Chip Application & Green Manufacturing Department, Industrial Technology Research Institute, Hsinchu 310401, Taiwan
    These authors contributed equally to this work.)

  • Ssu-Han Chen

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

Abstract

Integrated circuits (ICs) are critical components in the semiconductor industry, and precise wafer defect inspection is essential for maintaining product quality and yield. This study addresses the challenge of insufficient sample patterns in wafer defect datasets by using the denoising diffusion probabilistic model (DDPM) to produce generated defects that elevate the performance of wafer defect inspection. The quality of the generated defects was evaluated using the Fréchet Inception Distance (FID) score, which was then synthesized with real defect-free backgrounds to create an augmented defect dataset. Experimental results demonstrated that the augmented defect dataset significantly boosted performance, achieving 98.7% accuracy for YOLOv8-cls, 95.8% box mAP for YOLOv8-det, and 95.7% mask mAP for YOLOv8-seg. These results indicate that the generated defects produced by the DDPM can effectively enrich wafer defect datasets and enhance wafer defect inspection performance in real-world applications.

Suggested Citation

  • Ping-Hung Wu & Thi Phuong Hoang & Yen-Ting Chou & Andres Philip Mayol & Yu-Wei Lai & Chih-Hsiang Kang & Yu-Cheng Chan & Siou-Zih Lin & Ssu-Han Chen, 2024. "Elevating Wafer Defect Inspection with Denoising Diffusion Probabilistic Model," Mathematics, MDPI, vol. 12(20), pages 1-15, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3164-:d:1495416
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