패턴 불균형에 강건한 자가 지도학습을 활용한 웨이퍼 불량 패턴 클러스터링 방법 제안New Wafer Defect Pattern Clustering Method using a Self Supervised Learning Robust to Pattern Imbalance
- Other Titles
- New Wafer Defect Pattern Clustering Method using a Self Supervised Learning Robust to Pattern Imbalance
- Authors
- 최이수; 윤주호; 김병훈
- Issue Date
- Aug-2023
- Publisher
- 대한산업공학회
- Keywords
- Defect pattern clustering; Self-supervised learning; Semiconductor processing; Wafer bin map
- Citation
- 대한산업공학회지, v.49, no.4, pp 330 - 343
- Pages
- 14
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 49
- Number
- 4
- Start Page
- 330
- End Page
- 343
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115357
- DOI
- 10.7232/JKIIE.2023.49.4.330
- ISSN
- 1225-0988
2234-6457
- Abstract
- This study proposes a wafer defect pattern clustering model that can recognize defect patterns without the class label of the defect patterns. In the first step, noise defects are removed from each wafer bin map (WBM) image using the Depth-First Search (DFS) algorithm to clarify the defect pattern. Next, the defect patterns are clustered using the Dirichlet process, and the clustering results are adjusted by tuning the extracted features based on self-supervised learning. By employing a weighted cross-entropy loss that considers the cluster size, the model becomes robust to the imbalance of cluster sizes during the fine-tuning process. The proposed method can facilitate the identification and resolution of the causes of defects that occur during semiconductor processing.
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