• Transactions on Electrical and Electronic Materials
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• v.14 no.5
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• pp.254-257
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• 2013

In-situ optical emission spectroscopy (OES) is employed for PECVD chamber monitoring. OES is used as an addon sensor to monitoring and cleaning end point detection (EPD). On monitoring plasma chemistry using OES, the process gas and by-product gas are simultaneously monitored. Principal component analysis (PCA) enhances the capability of end point detection using OES data. Through chamber cleaning monitoring using OES, cleaning time is reduced by 53%, in general. Therefore, the gas usage of fluorine is also reduced, so satisfying Green Fab challenge in semiconductor manufacturing.

• Journal of the Semiconductor & Display Technology
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• v.16 no.2
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• pp.75-78
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• 2017

We reports improved monitoring performance of Self plasma-optical emission spectroscopy (SP-OES) by augmenting a by-pass tube to a conventional straight (or single) tube type self plasma reactor. SP-OES has been used as a tool for the monitoring of plasma chemistry indirectly in plasma process system. The benefits of SP-OES are low cost and easy installation, but some semiconductor industries who adopted commercialized SP-OES product experiencing less sensitivity and slow sensor response. OH out-gas chemistry monitoring was performed to have a direct comparison of a conventional single type tube and a by-pass type tube, and fluid dynamic simulation on the improved hardware design was also followed. It is observed faster pumping out of OH from the chamber in the by-pass type SP-OES.

• Journal of IKEEE
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• v.19 no.4
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• pp.541-547
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• 2015

In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in plasma etching process is more difficult than before. For endpoint detection, various kinds of sensors are installed in semiconductor manufacturing equipments, and sensor data are gathered with predefined sampling rate. Generally, detecting endpoint is performed using OES data of by-product. In this study, OES data of both by-product and etchant gas are used to improve reliability of endpoint detection. For the OES data pre-processing, a combination of Signal to Noise Ratio (SNR) and Principal Component Analysis (PCA),are used. Polynomial Regression and Expanded Hidden Markov model (eHMM) technique are applied to pre-processed OES data to detect endpoint.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.

• 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.93-98
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• 2023

As semiconductor devices become highly integrated and process difficulty increases, the need for highly sensitive sensors that can detect micro leaks is increasing. However, the noise contained in the CCD sensor itself acts as an obstacle to detecting fine leaks. In this study, integration time was changed for each condition, the sensor was cooled to 0℃, and the dark voltage level was measured to confirm through experiment the characteristics of the temporal noise included in the CCD sensor, a component of OES (Optical Emission Spectroscopy). When integration time was reduced from 30msec to 10msec, the dark voltage level decreased by about 20.5 % from an average of 151.5mV to 120.5mV. In the case of cooling device, Peltier elements were selected because of their simple structure and small size. During temperature cooling, the target temperature was controlled to within ±0.5℃ through PID control. When cooled from 20℃ to 0℃ using this cooling device, it was confirmed that the dark voltage level decreased by about 7% from an average of 147.0mV to 137.0mV.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.4
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• pp.395-401
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• 2013

In-situ optical emission spectroscopy (OES) is employed for leak detection in plasma etching system. A misprocessing is reported for significantly reduced silicon etch rate with chlorine gas, and OES is used as a supplementary sensor to analyze the gas phase species that reside in the process chamber. Potential cause of misprocessing reaches to chamber O-ring wear out, MFC leaks, and/or leak at gas delivery line, and experiments are performed to funnel down the potential of the cause. While monitoring the plasma chemistry of the process chamber using OES, the emission trace for nitrogen species is observed at the chlorine gas supply. No trace of nitrogen species is found in other than chlorine gas supply, and we found that the amount of chlorine gas is slightly fluctuating. We successfully found the root cause of the reported misprocessing which may jeopardize the quality of thin film processing. Based on a quantitative analysis of the amount of nitrogen observed in the chamber, we conclude that the source of the leak is the fitting of the chlorine mass flow controller with the amount of around 2-5 sccm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.06a
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• pp.126-126
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• 2009

Noninvasive plasma diagnostic technique is introduced to analyze and characterize HICP (Helmholtz Inductively Coupled Plasma) source during the plasma etching process. The HICP reactor generates plasma mainly through RF source power at 13.56MHz RF power and RF bias power of 12.56MHz is applied to the cathode to independently control ion density and ion energy. For noninvasive sensors, the RF sensor and the OES (Optical emission spectroscopy) were employed since it is possible to obtain both physical and chemical properties of the reactor with plasma etching. The plasma impedance and optical spectra were observed while altering process parameters such as pressure, gas flow, source and bias power during the poly silicon etching process. In this experiment, we have found that data measured from these noninvasive sensors can be correlated to etch results. In this paper, we discuss the relationship between process parameters and the measurement data from RF sensor and OES such as plasma impedance and optical spectra and using these relationships to analyze and characterize H-ICP source.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.64-64
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• 2009

As the advanced packaging technology developing, Copper electro-plating processing has be wildly utilized in the semiconductor interconnect technique. Chemical solution monitoring methods, including PH and gravity measurement exist in industry, but economical and practical real-time monitoring has not been achieved yet. Red-green-blue (RGB) color sensor can successfully monitor the condition of $CuSO_4$ solution during electric copper plating process. Comparing the intensity variations of the RGB data and optical spectroscopy data, strong correlation between two in-situ sensors have shown.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.9
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• pp.701-709
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• 2011

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.