• 산업공학
• /
• 제23권3호
• /
• pp.229-238
• /
• 2010

Virtual metrology (VM), a critical component of semiconductor manufacturing, is an efficient way of assessing the quality of wafers not actually measured. This is done based on a model between equipment sensor data (obtained for all wafers) and the quality characteristics of wafers actually measured. This paper considers principal component regression (PCR), partial least squares regression (PLSR), kernel PCR (KPCR), and kernel PLSR (KPLSR) as VM models. For each regression model, two cases are considered. One utilizes all explanatory variables in developing a model, and the other selects significant variables using the genetic algorithm (GA). The prediction performances of 8 regression models are compared for the short- and long-term etch process data. It is found among others that the GA-KPLSR model performs best for both types of data. Especially, its prediction ability is within the requirement for the short-term data implying that it can be used to implement VM for real etch processes.

• 대한산업공학회지
• /
• 제41권6호
• /
• pp.572-578
• /
• 2015

Yield prediction is one of the most important issues in semiconductor manufacturing. Especially, for a fast-changing environment of the semiconductor industry, accurate and reliable prediction techniques are required. In this study, we propose a prediction model to predict wafer yield based on virtual metrology process parameters in semiconductor manufacturing. The proposed prediction model addresses imbalance problems frequently encountered in semiconductor processes so as to construct reliable prediction model. The effectiveness and applicability of the proposed procedure was demonstrated through a real data from a leading semiconductor industry in South Korea.

• 반도체디스플레이기술학회지
• /
• 제20권4호
• /
• pp.146-150
• /
• 2021

Plasma information based virtual metrology (PI-VM) that predicts wafer-to-wafer etch rate variation after wet cleaning of plasma facing parts was developed. As input parameters, plasma information (PI) variables such as electron temperature, fluorine density and hydrogen density were extracted from optical emission spectroscopy (OES) data for etch plasma. The PI-VM model was trained by stepwise variable selection method and multi-linear regression method. The expected etch rate by PI-VM showed high correlation coefficient with measured etch rate from SEM image analysis. The PI-VM model revealed that the root cause of etch rate variation after the wet cleaning was desorption of hydrogen from the cleaned parts as hydrogen combined with fluorine and decreased etchant density and etch rate.

• 대한산업공학회지
• /
• 제38권1호
• /
• pp.46-56
• /
• 2012

The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer's metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.

• 반도체디스플레이기술학회지
• /
• 제18권4호
• /
• pp.30-34
• /
• 2019

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 (PI_EEDF), equipment, and optical emission spectroscopy (OES) features. In the procedure of stepwise variable selection (SVS), OES features are selected after PI_EEDF. 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 PI_EEDF. Securing PI_EEDF is important for the development of virtual metrology (VM) for prediction of process results. The role of PI_EEDF 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.

• 한국진공학회:학술대회논문집
• /
• 한국진공학회 2009년도 제38회 동계학술대회 초록집
• /
• pp.464-464
• /
• 2010

A few years ago, for maintaining high stability and production yield of production equipment in a semiconductor fab, on-line monitoring of wafers is required, so that semiconductor manufacturers are investigating a software based process controlling scheme known as virtual metrology (VM). As semiconductor technology develops, the cost of fabrication tool/facility has reached its budget limit, and reducing metrology cost can obviously help to keep semiconductor manufacturing cost. By virtue of prediction, VM enables wafer-level control (or even down to site level), reduces within-lot variability, and increases process capability, $C_{pk}$. In this research, we have practiced VM on $SiO_2$ etch rate with optical emission spectroscopy(OES) data acquired in-situ while the process parameters are simultaneously correlated. To build process model of $SiO_2$ via, we first performed a series of etch runs according to the statistically designed experiment, called design of experiments (DOE). OES data are automatically logged with etch rate, and some OES spectra that correlated with $SiO_2$ etch rate is selected. Once the feature of OES data is selected, the preprocessed OES spectra is then used for in-situ sensor based VM modeling. ICP-RIE using 葰.56MHz, manufactured by Plasmart, Ltd. is employed in this experiment, and single fiber-optic attached for in-situ OES data acquisition. Before applying statistical feature selection, empirical feature selection of OES data is initially performed in order not to fall in a statistical misleading, which causes from random noise or large variation of insignificantly correlated responses with process itself. The accuracy of the proposed VM is still need to be developed in order to successfully replace the existing metrology, but it is no doubt that VM can support engineering decision of "go or not go" in the consecutive processing step.

• Current Optics and Photonics
• /
• 제6권4호
• /
• pp.392-399
• /
• 2022

Light-measuring devices (LMDs) are frequently used to measure luminance and color coordinates of displays. However, it is very difficult to use a conventional LMD for measuring the optical properties of virtual-reality (VR) devices with a wide field of view (FOV), because of their confined spaces where the entrance pupil of a LMD is located. In this paper, a new LMD that can measure the optical properties of wide-FOV VR devices, without physical conflict with the goggles of the VR device, is proposed. The LMD is designed to fully satisfy the requirements of IEC 63145-20-10, and a pivot-point correction method for the LMD is applied to improve its accuracy. To show the feasibility of the developed LMD and the correction method, seven VR devices with wide FOV are measured with it. From the results, all of them are successfully measured without any physical conflict, and a comparison to their nominal values shows that the FOVs have been properly measured.

• 한국진공학회:학술대회논문집
• /
• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
• /
• pp.132-132
• /
• 2015

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification (FDC) or advanced process control (APC) models on to the real mass production lines efficiently, high performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this study, as an effective method to include the 'good information' representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b-, q-factors and surface passivation parameters as PCs into the PCR based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient data set provided cases. For mass production data of 350 wafers, developed PI based VM (PI-VM) model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.

• 한국음향학회지
• /
• 제38권6호
• /
• pp.637-650
• /
• 2019

이 논문은 가상현실 오디오에서 널리 사용되는 Ambisonics에 기반하여 헤드폰을 통해 재생하는 binaural sound의 한계를 분석한 것이다. 가상현실 오디오는 청자의 머리 움직임을 보상하는 binaural sound를 통해 제공된다. Ambisonics는 가상현실 오디오에서 청자를 둘러싼 배경음장을 레코딩하고 재생하는데에 널리 사용되는데, 1차 Ambisonics가 간단하다는 장점 때문에 여전히 가상현실 오디오에서 사용되고 있다. 그러나, 물리적인 관점에서 1차의 상한 주파수는 너무 낮아서 귀 위치의 신호를 완벽히 재현하지 못한다. 따라서 이렇게 재생된 binaural sound는 스펙트럼과 음원 위치 형성에서 근본적인 한계를 갖는다. 이 논문은 이러한 한계를 기준 음장과 재생 음장에서의 귀 위치의 신호 비교를 통해 알아 본다. 하나의 입사파를 기준 음장으로 정의하고, 이 것을 가상 스피커를 이용해서 Ambisonics를 통해 재생한다. 주파수 응답, 양이 레벨차, 양이 위상차가 비교된다. 비교 결과, 상한 주파수 이상에서 재생음장의 음압 레벨은 감소하고 수평면 상에서의 음원 위치는 청자의 정면 방향 근처에서만 잘 형성됨을 알 수 있었다.

• The Journal of Advanced Prosthodontics
• /
• 제14권3호
• /
• pp.150-161
• /
• 2022

PURPOSE. The purpose of the study was to assess the influence of build orientations and density of support structures on the trueness of the 3D printed removable partial denture (RPD) frameworks. MATERIALS AND METHODS. A maxillary Kennedy class III and mandibular class I casts were 3D scanned and used to design and produce two 3D virtual models of RPD frameworks. Using digital light processing (DLP) 3D printing, 47 RPD frameworks were fabricated at 3 different build orientations (100, 135 and 150-degree angles) and 2 support structure densities. All frameworks were scanned and 3D compared to the original virtual RPD models by metrology software to check 3D deviations quantitatively and qualitatively. The accuracy data were statistically analyzed using one-way ANOVA for build orientation comparison and independent sample t-test for structure density comparison at (α = .05). Points study analysis targeting RPD components and representative color maps were also studied. RESULTS. The build orientation of 135-degree angle of the maxillary frameworks showed the lowest deviation at the clasp arms of tooth 26 of the 135-degree angle group. The mandibular frameworks with 150-degree angle build orientation showed the least deviation at the rest on tooth 44 and the arm of the I-bar clasp of tooth 45. No significant difference was seen between different support structure densities. CONCLUSION. Build orientation had an influence on the accuracy of the frameworks, especially at a 135-degree angle of maxillary design and 150-degree of mandibular design. The difference in the support's density structure revealed no considerable effect on the accuracy.