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논문 기본 정보

자료유형
학술저널
저자정보
Younji Lee (Myongji University) Jeong Eun Choi (Myongji University) Surin An (Myongji University) Sang Jeen Hong (Myongji University)
저널정보
한국진공학회(ASCT) Applied Science and Convergence Technology Applied Science and Convergence Technology Vol.33 No.6
발행연도
2024.11
수록면
181 - 188 (8page)

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This paper proposes an equipment intelligence study that builds an integrated database system to collect equipment and sensor data gener- ated during real-time processes, which are in turn used to diagnose abnormalities in semiconductor manufacturing equipment. The integrated database system leverages edge computing to convert data collected simultaneously from equipment and sensors into standardized communi- cation protocols on a single central server. The constructed system not only detects equipment abnormalities in advance, but also helps identify the exact cause of these abnormalities by collecting optical emission spectroscopy sensor data generated from the equipment, such as voltage, pressure, and gas flow generated during the SiO₂ deposition process. The system analyzes the results by determining meaningful data from the collected data and feeding them into a fault detection and classification algorithm. This research is promising for greatly improving the efficiency and yield of the overall continuous semiconductor manufacturing process.

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ABSTRACT
1. Introduction
2. Database system
3. Experiment
4. Results
5. Consideration

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