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YOLOSeg with applications to wafer die particle defect segmentation.

Yen-Ting Li1, Yu-Cheng Chan2, Chen-Che Huang3

  • 1Circle AI Incorporation, Taipei, 114, Taiwan.

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|January 17, 2025
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Summary

This study introduces You Only Look Once Segmentation (YOLOSeg), an efficient model for detecting small particle defects on wafer dies. YOLOSeg achieves high accuracy in segmenting defects, outperforming existing methods and simplifying training data preparation.

Keywords:
Auto-annotationDefect segmentationDenoising diffusion probabilistic models (DDPM)Wafer dieYou only look once (YOLO)

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Area of Science:

  • Computer Vision
  • Materials Science
  • Semiconductor Manufacturing

Background:

  • Accurate detection of small particle defects on wafer dies is crucial for semiconductor quality control.
  • Existing instance segmentation models often struggle with the scale and subtlety of these defects.
  • Efficient training data preparation is a significant bottleneck in developing robust defect detection systems.

Purpose of the Study:

  • To develop an end-to-end instance segmentation model, YOLOSeg, for precise identification and segmentation of small particle defects on wafer dies.
  • To enhance the model's performance through optimized training strategies and advanced augmentation techniques.
  • To demonstrate YOLOSeg's superiority over current state-of-the-art methods in terms of accuracy and efficiency.

Main Methods:

  • Development of YOLOSeg, an instance segmentation model based on YOLOv5s, incorporating a UNet-like segmentation head.
  • Implementation of training optimizations including layer freezing, mask loss switching, auto-anchor utilization, and denoising diffusion probabilistic models (DDPM) for image augmentation.
  • Evaluation of YOLOSeg's performance using average precision (AP) and intersection over union (IoU) metrics on a dedicated testing dataset.

Main Results:

  • YOLOSeg achieved high performance metrics, with an average precision (AP) of 0.821 and an intersection over union (IoU) of 0.732.
  • The model demonstrated superior performance in segmenting extremely small particle defects compared to established models like Mask R-CNN, YOLACT, YUSEG, and YOLOv5s-segmentation.
  • The training process for YOLOSeg requires less time and effort, eliminating the need for extensive data collection, pseudo-defect annotation, or complex feature engineering.

Conclusions:

  • YOLOSeg is a highly effective and efficient model for instance segmentation of small particle defects in semiconductor manufacturing.
  • The proposed training strategies and DDPM augmentation significantly improve segmentation accuracy, especially for challenging small-scale defects.
  • YOLOSeg offers a practical and time-saving solution for wafer defect inspection, reducing the burden of data preparation and enhancing quality control.