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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>
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.
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.
PI-VM을 이용한 용량 결합 Ar/SF6/O2 플라즈마에서의 전력 인가 에지 링 식각 특성 조사
김곤호,이현주,송재민,박태준,김남균 한국반도체디스플레이기술학회 2023 반도체디스플레이기술학회지 Vol.22 No.4
The edge ring placed on the outside of the electrostatic chuck (ESC) is a key component for protecting the ESC and controlling the etching uniformity of the edge of the wafer. Therefore, it is very important to understand the etching phenomenon of edge rings for edge ring management and equipment homeostasis. In this study, a specimen with SiO2 hard mask and underlying Si mold was installed on the edge ring surface and the etching results were measured by varying the edge ring 2MHz RF power. By developing PI-VM model with high prediction accuracy and analyzing the roles of key parameters in the model, we were able to evaluate the effect of plasma and sheath characteristics around the edge ring on edge ring erosion. This analysis method provided information necessary for edge ring maintenance and operation.
Song Jaemin,Lee Myeonggeon,Ryu Sangwon,Jang Yunchang,Park Seolhye,Kim Gon-Ho 한국물리학회 2023 Current Applied Physics Vol.45 No.-
Ion-induced etch damage on trench surfaces of Ge2Sb2Te5 (GST) by reactive ion etching (RIE) is investigated with Ar/SF6 capacitively coupled plasma (CCP). Etch damage on the sidewall increases with higher plasma density and decreases with the bias power. The roughness of the bottom decreases with the bias power due to the ion sputtering. It is demonstrated that the optimum process condition to minimize the ion-induced damage can be obtained from the feature analysis of the virtual metrology, which was developed with plasma information parameters named PI-VM, and it predicted the GST etch rate and surface roughness. It reveals that the energetic ions play a crucial role in removing the halogenated surfaces by high-energy ion sputtering. In addition, the sidewall damage by the lower F radicals is significant because the collisional diffused F radicals are enhanced in the high-density plasma. It explains the control of bias power required to achieve the profiling etch in the high-density plasma.