인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
In semiconductors, various defect patterns appear on the wafer map due to problems in the design and manufacturing process. Analysis of generated defect patterns will reduce the rate of defects and enable the production of high-quality semiconductors. Considering the number of semiconductors produced, performing defect analysis by human resources is inefficient. Recently, as hardware performance has improved, high-performance deep learning models have been designed. These models show high performance in image classification and have advantages in terms of processing speed. Therefore, this paper used EfficientNetV2 designed to achieve maximum efficiency with few parameters for semiconductor failure analysis. Identifying the location of defects is a critical element of defect analysis, not just classifying defect patterns. Therefore, in this study, we used Grad-CAM to identify the classification of defect patterns and their approximate location. Wafer map dataset is difficult to collect as data includes defects in manufacturing companies" processes. To train EfficientNetV2, we used the WM-811K dataset, a publicly available dataset on Kaggle. This dataset has an imbalance in the number of data between classes. We increased the data using Flip and Rotate to address the dataset"s imbalances, ultimately improving the classification performance. The test results showed an accuracy of 0.944 and an F1-score of 0.929.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-ECN-0101-2023-560-001328017
