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스마트무인기연료공급시스템 연료이송 제트펌프의 설계 및 성능해석에 관한 연구
박설혜(Sulhye Park),이윤권(Yoonkwon Lee),이지근(Jeekeun Lee),이창호(Changho Lee),이수철(Soochul Lee),최희주(Heejoo Choi) 한국항공우주학회 2007 韓國航空宇宙學會誌 Vol.35 No.11
스마트무인기 연료공급시스템에 사용되는 연료이송 제트펌프의 1차원 유동해석을 통한 설계가 이루어졌으며 성능 검증 및 설계 개선점을 찾고자 상용코드를 이용하여 전산해석이 수행되었다. 해석적 연구 결과로부터 제트펌프는 스마트무인기 연료공급시스템에서 요구하는 유량비 2.23을 만족하는 설계가 이루어졌음이 확인되었다. 구동노즐에 작용하는 압력은 낮게 예측된 손실계수로 인하여 전산해석의 경우가 1차원 유동해석을 통한 설계값보다 더 높은 값을 나타냈다. 결과적으로 제트펌프 구성 요소의 정확한 크기 결정을 위해서는 각 주요부에서의 손실계수에 대한 정확한 데이터가 필요하며, 데이터는 측정위치 및 측정대상의 기하학적 형상 정보와 함께 제공되어져야 한다. Design and performance analysis of the jet pump to transfer fuel between tanks in the smart UAV fuel supply system were carried out through one dimensional flow analysis and the flow analysis using a commercial CFD code. From the analysis results, it was proved that the jet pump was designed with the flow ratio of 2.23 that is the fundamental requirement of the jet pump design. The comparison results showed that the primary nozzle pressure is higher in the CFD analysis than in one dimensional flow analysis, mainly due to the underestimated loss coefficient of the primary nozzles. Consequently, the loss coefficients of the jet pump components should be determined more precisely for the design of the jet pumps with high performance.
플라즈마 정보인자를 활용한 SiO<sub>2</sub> 식각 깊이 가상 계측 모델의 특성 인자 역할 분석
장윤창,박설혜,정상민,유상원,김곤호,Jang, Yun Chang,Park, Seol Hye,Jeong, Sang Min,Ryu, Sang Won,Kim, Gon Ho 한국반도체디스플레이기술학회 2019 반도체디스플레이기술학회지 Vol.18 No.4
We analyzed how the features in plasma information based virtual metrology (PI-VM) for SiO2 etching depth with variation of 5% contribute to the prediction accuracy, which is previously developed by Jang. As a single feature, the explanatory power to the process results is in the order of plasma information about electron energy distribution function (PIEEDF), equipment, and optical emission spectroscopy (OES) features. In the procedure of stepwise variable selection (SVS), OES features are selected after PIEEDF. Informative vector for developed PI-VM also shows relatively high correlation between OES features and etching depth. This is because the reaction rate of each chemical species that governs the etching depth can be sensitively monitored when OES features are used with PIEEDF. Securing PIEEDF is important for the development of virtual metrology (VM) for prediction of process results. The role of PIEEDF as an independent feature and the ability to monitor variation of plasma thermal state can make other features in the procedure of SVS more sensitive to the process results. It is expected that fault detection and classification (FDC) can be effectively developed by using the PI-VM.
플라즈마 정보인자를 활용한 SiO2 식각 깊이 가상 계측 모델의 특성 인자 역할 분석
장윤창,박설혜,정상민,유상원,김곤호 한국반도체디스플레이기술학회 2019 반도체디스플레이기술학회지 Vol.18 No.4
We analyzed how the features in plasma information based virtual metrology (PI-VM) for SiO2 etching depth with variation of 5 % contribute to the prediction accuracy, which is previously developed by Jang. As a single feature, the explanatory power to the process results is in the order of plasma information about electron energy distribution function (PIEEDF), equipment, and optical emission spectroscopy (OES) features. In the procedure of stepwise variable selection (SVS), OES features are selected after PIEEDF. Informative vector for developed PI-VM also shows relatively high correlation between OES features and etching depth. This is because the reaction rate of each chemical species that governs the etching depth can be sensitively monitored when OES features are used with PIEEDF. Securing PIEEDF is important for the development of virtual metrology (VM) for prediction of process results. The role of PIEEDF as an independent feature and the ability to monitor variation of plasma thermal state can make other features in the procedure of SVS more sensitive to the process results. It is expected that fault detection and classification (FDC) can be effectively developed by using the PI-VM.
장윤창,노현준,박설혜,정상민,Sanywon Ryu,권지원,김남균,김곤호 한국물리학회 2019 Current Applied Physics Vol.19 No.10
A phenomenology-based virtual metrology (VM) for monitoring SiO2 etching depth was proposed by Park (2015). It achieved high prediction accuracy by introducing newly developed plasma information (PI) variables as designated inputs, called PI-VM. The PI variables represent the state of the plasma, the sheath, and the target during the process. We investigate how a PI variable can help to improve prediction accuracy of VM and how it plays a special role in the statistical selection. We choose only PIEEDF among the three PI variables to focus on the investigation. The PIEEDF is determined from the ratio of line-intensities of optical emission spectroscopy. We apply Pearson's correlation filter (PCF), principal component analysis (PCA), and stepwise variable selection (SVS) as statistical selection methods on the variables set including PIEEDF or not. Multilinear regression is used to model the VM. This study reveals that PIEEDF variable is a good variable in terms of independence from other input variables and explanatory power for an output variable. Especially, VM using SVS method applied to variable sets including PIEEDF achieves the highest accuracy, comparable to Park's PI-VM. This study shows that PIEEDF variable is particularly useful for monitoring of the fine variations in semiconductor manufacturing process and it also extends the utilization of OES sensor data.