Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Detection of Defect Patterns on Wafer Bin Map Using Fully Convolutional Data Description (FCDD)

FCDD 기반 웨이퍼 빈 맵 상의 결함패턴 탐지

  • Seung-Jun Jang (Department of Industrial Engineering, Hanyang University) ;
  • Suk Joo Bae (Department of Industrial Engineering, Hanyang University)
  • Received : 2023.05.17
  • Accepted : 2023.06.22
  • Published : 2023.06.30
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Abstract

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

Keywords

Acknowledgement

This study is a research project conducted with the support of the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Energy Technology Evaluation Institute (KETEP). (No. 2021202090056C, 20213030030190)

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