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RISS 인기검색어
- 검색키워드
- 저자 : Sanywon Ryu (검색결과 2 건)
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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.
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Jang, Yunchang,Roh, Hyun-Joon,Park, Seolhye,Jeong, Sangmin,Ryu, Sanywon,Kwon, Ji-Won,Kim, Nam-Kyun,Kim, Gon-Ho Elsevier 2019 Current Applied Physics Vol.19 No.10
<P><B>Abstract</B></P> <P>A phenomenology-based virtual metrology (VM) for monitoring SiO<SUB>2</SUB> 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 PI<SUB>EEDF</SUB> among the three PI variables to focus on the investigation. The PI<SUB>EEDF</SUB> 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 PI<SUB>EEDF</SUB> or not. Multilinear regression is used to model the VM. This study reveals that PI<SUB>EEDF</SUB> 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 PI<SUB>EEDF</SUB> achieves the highest accuracy, comparable to Park's PI-VM. This study shows that PI<SUB>EEDF</SUB> variable is particularly useful for monitoring of the fine variations in semiconductor manufacturing process and it also extends the utilization of OES sensor data.</P>
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