• 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.

• 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 Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.46-56
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• 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.

• IE interfaces
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• v.23 no.3
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• pp.229-238
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• 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.

• 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.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.464-464
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• 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.

• 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.18 no.2
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• pp.48-52
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• 2019

Virtual metrology, which is one of APC techniques, is a method to predict characteristics of manufactured films using machine learning with saving time and resources. As the photoresist is no longer a mask material for use in high aspect ratios as the CD is reduced, hard mask is introduced to solve such problems. Among many types of hard mask materials, amorphous carbon layer(ACL) is widely investigated due to its advantages of high etch selectivity than conventional photoresist, high optical transmittance, easy deposition process, and removability by oxygen plasma. In this study, VM using different machine learning algorithms is applied to predict the thickness of ACL and trained models are evaluated which model shows best prediction performance. ACL specimens are deposited by plasma enhanced chemical vapor deposition(PECVD) with four different process parameters(Pressure, RF power, $C_3H_6$ gas flow, $N_2$ gas flow). Gradient boosting regression(GBR) algorithm, random forest regression(RFR) algorithm, and neural network(NN) are selected for modeling. The model using gradient boosting algorithm shows most proper performance with higher R-squared value. A model for predicting the thickness of the ACL film within the abovementioned conditions has been successfully constructed.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.875-880
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• 2015

Traditional semiconductor process control has been performed through statistical process control techniques in a constant process-recipe conditions. However, the complexity of the interior of the etching apparatus plasma physics, quantitative modeling of process conditions due to the many difficult features constraints apply simple SISO control scheme. The introduction of the Advanced Process Control (APC) as a way to overcome the limits has been using the APC process control methodology run-to-run, wafer-to-wafer, or the yield of the semiconductor manufacturing process to the real-time process control, performance, it is possible to improve production. In addition, it is possible to establish a hierarchical structure of the process control made by the process control unit and associated algorithms and etching apparatus, the process unit, the overall process. In this study, the research focused on the methodology and monitoring improvements in performance needed to consider the process management of future developments in the semiconductor manufacturing process in accordance with the age of the APC analysis in real applications of the semiconductor manufacturing process and process fault diagnosis and control techniques in progress.