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Chip Appearance Defect Recognition Based on Convolutional Neural Network.

Jun Wang1,2, Xiaomeng Zhou2, Jingjing Wu2

  • 1School of Mechanical Technology, Wuxi Institute of Technology, Wuxi 214121, China.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary

This study introduces a convolutional neural network algorithm for identifying chip appearance defects. It enhances recognition accuracy and reduces training time by automatically cleaning data samples, achieving over 99.5% accuracy.

Keywords:
chip appearance defectsconvolutional neural networkdata cleaningpattern recognition

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Area of Science:

  • Materials Science
  • Computer Science
  • Electrical Engineering

Background:

  • Chip appearance defects can significantly impact product quality and reliability.
  • Existing defect recognition methods often suffer from long training times and low accuracy due to redundant data.

Purpose of the Study:

  • To develop an efficient and accurate algorithm for identifying chip appearance defects.
  • To reduce the training time and improve the recognition accuracy of defect classification models.

Main Methods:

  • Image processing and region-of-interest extraction to locate defects.
  • An automatic data sample cleaning algorithm based on prior knowledge to remove redundant samples.
  • Construction of a convolutional neural network (CNN) model for defect classification.

Main Results:

  • The proposed algorithm achieved a zero miss detection rate.
  • The accuracy rate for chip appearance defect recognition exceeded 99.5%.
  • The data cleaning process effectively reduced training and classification time.

Conclusions:

  • The developed CNN-based algorithm with data cleaning is highly effective for recognizing chip appearance defects.
  • The algorithm meets stringent industry requirements for accuracy and efficiency.
  • This approach offers a robust solution for automated quality control in semiconductor manufacturing.