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
With advancements in technology, the importance of customized wafer manufacturing has been increasing, particularly as the demand for precise manufacturing requirements, such as wafer slope, continues to expand. However, relying solely on simple statistical analysis or empirical approaches has limitations in effectively improving the yield of customized production. Therefore, manufacturers must adopt a data-based approach to respond effectively to these evolving requirements. This study explores the key factors influencing wafer slope adjustment for yield improvement by effectively responding to wafer slope requirements through a predictive model based on manufacturing process data. To achieve this, multiple linear regression and deep learning models were developed to control the manufacturing process for precise wafer slope implementation, and their performance was comparatively analyzed. The experimental results indicate that the key factors influencing wafer slope and their effects vary depending on wafer slope requirements and product type. Additionally, it was found that effective data collection and utilization, along with selecting an appropriate model, are essential for accurately representing product-specific requirements. Through this study, various considerations regarding data utilization for demand-driven quality management were examined, and the findings are expected to contribute to the development of strategies for improving the yield of related manufacturing processes.
본문·목차
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