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.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
As semiconductor manufacturing continues to scale down, the precision and reliability of high-resolution optical inspection equipment have become critical to maintaining process yield. However, variations in hardware sensitivity and parameter settings among identical tools often lead to inconsistencies in defect detection, undermining the stability of process control. This study proposes an automated hardware matching framework that combines a structured calibration sample fabricated using a Particle Deposition System (PDS) with a residual matrix-based linear interpolation algorithm. The proposed method enables automatic alignment of sensitivity responses across tools by quantifying detection discrepancies and deriving optimal settings for laser power and time delay integration (TDI) gain. Applied to 191 optical inspection tools in a Samsung Electronics high-volume manufacturing (HVM) line, the approach achieved a sensitivity matching accuracy within ±2% and reduced preventive maintenance (PM) time by 45%. Deployed across 191 IS4100 patterned-wafer optical inspectors operating at after-develop (ADI), after-etch (AEI), and post-CMP inspection (PCI) checkpoints, the matched fleet provided consistent in-line gating of excursions, aligning with the observed 0.41-pp improvement in production yield. This framework demonstrates a significant advancement over manual calibration approaches and validates its potential as a core enabling technology for Smart Fab and Unattended Fab environments.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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