• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.146-150
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• 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.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.132-132
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• 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.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.30-34
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• 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.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.7-12
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• 2023

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.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.53-58
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• 2022

Shape changes of hard mask play a key role in the aspect ratio dependent etch (ARDE). For etch process using high density and energy ions, deformation of hard mask shape becomes more severe, and high aspect ratio (HAR) etch profile is distorted. In this study, polygonal geometric model for shape-deformation of amorphous carbon layered hard mask is suggested to control etch profile during the process. Mask shape is modeled with polygonal geometry consisting of trapezoids and rectangles, and it provides dynamic information about angles of facets and etched width and height of remained mask shape, providing important features for real-time HAR etch profiling.

• Animal Bioscience
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• v.35 no.2
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• pp.224-235
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• 2022

Objective: Cage rearing has critical implications for the laying duck industry because it is convenient for feeding and management. However, caging stress is a type of chronic stress that induces maladaptation. Environmental stress responses have been extensively studied, but no detailed information is available about the comprehensive changes in plasma metabolites at different stages of caging stress in ducks. We designed this experiment to analyze the effects of caging stress on performance parameters and oxidative stress indexes in ducks. Methods: Liquid chromatography tandem mass spectrometry (LC/MS-MS) was used to determine the changes in metabolites in duck plasma at 5 (CR5), 10 (CR10), and 15 (CR15) days after cage rearing and traditional breeding (TB). The associated pathways of differentially altered metabolites were analyzed using Kyoto encyclopedia of genes and genomes (KEGG) database. Results: The results of this study indicate that caging stress decreased performance parameters, and the plasma total superoxide dismutase levels were increased in the CR10 group compared with the other groups. In addition, 1,431 metabolites were detected. Compared with the TB group, 134, 381, and 190 differentially produced metabolites were identified in the CR5, CR10, and CR15 groups, respectively. The results of principal component analysis (PCA) show that the selected components sufficiently distinguish the TB group and CR10 group. KEGG analysis results revealed that the differentially altered metabolites in duck plasma from the CR5 and TB groups were mainly associated with ovarian steroidogenesis, biosynthesis of unsaturated fatty acids, and phenylalanine metabolism. Conclusion: In this study, the production performance, blood indexes, number of metabolites and PCA were compared to determine effect of the caging stress stage on ducks. We inferred from the experimental results that caging-stressed ducks were in the sensitive phase in the first 5 days after caging, caging for approximately 10 days was an important transition phase, and then the duck continually adapted.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.28-34
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• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.2
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• pp.67-75
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• 2002

The human complement receptor type 1 (CR 1, C3 b/C4b receptor) is a polymorphic membrane glycoprotein expressed on human erythrocytes, peripheral leukocytes, plasma and renal glomerular podocytes, which consists of transmembrane and cytoplasmic domains with 30 repeating homologous protein domains known as short consensus repeats (SCR). CR1 has been used as an inhibitor for inflammatory and immune system for the past several years. Recently; it is reported that CRl was found to suppress the hyper-acute rejection in xeno-transplantation and can be used to cure autoimmune diseases. A soluble form of CRl, called sCRl, is a recombinant CRl by cleaving the transmembrane domain at C-terminus and has been expressed in Chinese Hamster Ovary (CHO) cells. Several purification methods for sCR1 from CHO cells have been reported, but most of them require complicated steps at high cost. Moreover, such methods are mostly performed under the pH condition apt to denaturing sCR1 and causes sCRl losing its activity. We here report a rapid and efficient method to purify sCR1 from CHO cell. The new method consists of a two-stage of cell culture by cultivating cells in serum medium followed by serum-free medium, and a two-stage of column purification by means of heparin and gel filtration column chromatography. By using this novel method, sCR1 can be purified in a simple and effective way with high yield and purity, furthermore, the purified sCR1 was confirmed to retain its activity to suppress the complement activation in vivo and ex vivo.

• Journal of the Korean Vacuum Society
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• v.6 no.4
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• pp.348-353
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• 1997

Hydrogenated amorphous carbon(a-C:H) films were deposited on p-type Si(100) by DC saddle-field plasma enhanced CVD to investigate the effect of substrate bias on optical properties and structural changes. They were deposited using pure methane gas at a wide range of substrate bias at room temperature and 90 mtorr. The substrate bias voltage ($V_s$) was employed from $V_s=0 V$ to $V_s=400 V$. The information of optical properties was investigated by photoluminescence and transmitance. Chemical bondings of a-C:H have been explored from FT-IR and Raman spectroscopy. The thickness and relative hydrogen content of the films were measured by Rutherford backscattering spectroscopy (RBS) and elastic recoil detection (ERD) technigue. The growth rate of a-C:H film was decreased with the increase of $V_s$, but the hydrogen content of the film was increased with the increase of $V_s$. The a-C:H films deposited at the lowest $V_s$ contain the smallest amount of hydrogen with most of C-H bonds in the of $CH_2$ configuration, whereas the films produced at higher $V_s$ reveal dominant the $CH_3$ bonding structure. The emission of white photoluminescence from the films were observed even with naked eyes at room temperature and the PL intensity of the film has the maximum value at $V_s$=200 V. With $V_s$ lower than 200 V, the PL intensity of the film increased with V, but for V, higher than 200 V, the PL intensity decreased with the increase of $V_s$. The peak energy of the PL spectra slightly shifted to the higher energy with the increase of $V_s$. The optical bandgap of the film, determined by optical transmittance, was increased from 1.5 eV at $V_s$=0V to 2.3 eV at $V_s$=400 V. But there were no obvious relations between the PL peak and the optical gap which were measured by Tauc process.