Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching

Hong, Sang Jeen; May, G.S.

doi:10.1109/tie.2005.851663

Cited by 37 publications

(26 citation statements)

References 9 publications

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“…Engineering principle that was employed in this experiment is Ar actinometry and this was used for the analysis of OES data which was acquired from silicon oxide etching process. The split experiment consists of four levels of CF 4 flow from 30 to 60 sccm with 5 sccm of Ar and throughout the experimental run 500 watts of 13.56 MHz RF power was applied while the base pressure was maintained at 10 mTorr. In order to monitor the plasma optical emission, the optical fiber was mounted on a sidewall viewport and the detection range was about 180 nm to 990 nm.…”

Section: Chemical Actinometrymentioning

confidence: 99%

“…Emission actinometric analysis was performed by comparing the observed intensity of C, CF, and CF 2 in their magnitudes from two data sets; normal and abnormal process. Plasma processing uses CF 4 , as CF 3 , CF 2 , and CF radicals. Moreover, the ions that result from the electron impact ionization of CF 4 , are one of the most abundant and reactive species.…”

Section: Analysis Of Abnormal Etch Processmentioning

confidence: 99%

“…In order to detect the electrical character of plasma, electrical devices, such as self-excited electron resonance spectroscopy (SEERS) and IV-probes have been used [2,3]. Plasma process monitoring which uses optical emission spectroscopy (OES) has also been suggested as non-invasive monitoring [4].…”

Section: Introductionmentioning

confidence: 99%

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Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO₂Plasma Etch

Kim

Hong

2012

Transactions on Electrical and Electronic Materials

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Optical emission spectroscopy (OES) is often used for real-time analysis of the plasma processes. OES has been suggested as a primary plasma process monitoring tool. It has the advantage of non-invasive in-situ monitoring capability but selecting the proper wavelengths for the analysis of OES data generally relies on empirically established methods. In this paper, we propose a practical method for the selection of OES wavelength peaks for the analysis of plasma etch process and this is done by investigating reactants and by-product gas species that reside in the plasma etch chamber. Wavelength selection criteria are based on the standard deviation and correlation coefficients. Moreover, chemical actinometry is employed for the normalization of the selected wavelengths. We also present the importance of chemical actinometry of OES data for quantitative analysis of plasma. Then, the suggested OES peak selection method is employed.. This method is used to find out the reason behind abnormal etching of PR erosion during a series of SiO 2 etch processes using the same recipe. From the experimental verification, we convinced that OES is not only capable for real-time detection of abnormal plasma process but it is also useful for the analysis of suspicious plasma behavior.

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Section: Chemical Actinometrymentioning

confidence: 99%

Section: Analysis Of Abnormal Etch Processmentioning

confidence: 99%

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Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO₂Plasma Etch

Kim

Hong

2012

Transactions on Electrical and Electronic Materials

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“…The unexpected introduction of nitrogen forms a silicon nitride nonvolatile by-product, and this material may act as an etch stop or etch inhibitor during the process. However, finding the source of nitrogen introduction is a challenge as the many possibilities include a chamber leak from Orings, mass flow controller (MFC) mis-calibration, gas delivery line leaks, and etc [7].…”

Section: Experimental Apparatusmentioning

confidence: 99%

Use of In-Situ Optical Emission Spectroscopy for Leak Fault Detection and Classification in Plasma Etching

Lee¹,

Seo²,

May³

et al. 2013

JSTS:Journal of Semiconductor Technology and Science

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Abstract-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.

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“…In-situ monitoring and real-time fault diagnosis in a unit process supports how the precise manufacturing process control became necessary [1]. To achieve manufacturing effectiveness, semiconductor industry adopted advanced process control (APC) for their manufacturing, complete understanding of unit process cannot be over emphasized in order to ensure a successful integration of a number of semiconductor fabrication processes in ultra large scale integration (ULSI) era.…”

Section: Introductionmentioning

confidence: 99%

Multiple-inputs Dual-outputs Process Characterization and Optimization of HDP-CVD SiO₂Deposition

Hong

Hwang²,

Chun³

et al. 2011

JSTS:Journal of Semiconductor Technology and Science

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Abstract-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 Å/sec deposition rate.

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Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching

Cited by 37 publications

References 9 publications

Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO₂Plasma Etch

Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO₂Plasma Etch

Use of In-Situ Optical Emission Spectroscopy for Leak Fault Detection and Classification in Plasma Etching

Multiple-inputs Dual-outputs Process Characterization and Optimization of HDP-CVD SiO₂Deposition

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Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching

Cited by 37 publications

References 9 publications

Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO2Plasma Etch

Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO2Plasma Etch

Use of In-Situ Optical Emission Spectroscopy for Leak Fault Detection and Classification in Plasma Etching

Multiple-inputs Dual-outputs Process Characterization and Optimization of HDP-CVD SiO2Deposition

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Product

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About

Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO₂Plasma Etch

Actinometric Investigation of In-Situ Optical Emission Spectroscopy Data in SiO₂Plasma Etch

Multiple-inputs Dual-outputs Process Characterization and Optimization of HDP-CVD SiO₂Deposition