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.

1. 서 론
2. 이론적 배경
    2.1 이미지 필터
    2.2 합성곱 신경망 기반 이상탐지
3. 제안 프로세스
    3.1 전반적인 프로세스
    3.2 이중필터 패턴 디노이징
    3.3 커스터마이징 FCDD
    3.4 베이지안 최적화 기반 임계점 설정
4. 분석 결과
    4.1 데이터 설명
    4.2 이중필터 패턴 디노이징
5. 결론 및 향후 연구 제안
Acknowledgement
References

• Seung-Jun Jang(Department of Industrial Engineering, Hanyang University) | 장승준 (한양대학교 산업공학과)
• Suk Joo Bae(Department of Industrial Engineering, Hanyang University) | 배석주 (한양대학교 산업공학과) Corresponding author