FCDD 기반 웨이퍼 빈 맵 상의 결함패턴 탐지

• 영문명: Detection of Defect Patterns on Wafer Bin Map Using Fully Convolutional Data Description (FCDD)
• 발행기관: 한국산업경영시스템학회
• 저자명: 장승준 배석주
• 간행물 정보: 『산업경영시스템학회지』제46권 제2호, 1~12쪽, 전체 12쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.06.30

영문 초록

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.

목차

1. 서 론

2. 이론적 배경

3. 제안 프로세스

4. 분석 결과

5. 결론 및 향후 연구 제안

Acknowledgement

References

키워드

Wafer Bin Map Defect Patterns Anomaly Detection Explainable AI(XAI) Fully Convolutional Data Description (FCDD)