Characterization of step coverage change in ultraviolet-transparent plasma enhanced chemical vapor deposition silicon nitride films

Bierner, Jessica Anne; Jacob, Mohan V.; Schönherr, Helmut

doi:10.1116/1.1314394

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…This was experimentally ascertained in previous studies, in which the RI was strongly correlated to the Si/N ratio [1] or SiH/NH [7]. It has also been observed that the relationship is valid only at higher SiH NH ratio [2], [7]. The increase is consistently observed for all temperatures.…”

Section: Effects Of Nh Flow Rate and Substratesupporting

confidence: 73%

“…The represents the gradient of the bipolar sigmoid function and determines the activation level of neuron. Meanwhile, the BPNN adopts a linear function in the output layer, expressed as (2) where represents the gradient of the linear function. For a given set of training factors, both gradients in (1) and (2) are to be optimized to improve the prediction accuracy.…”

Section: Fundamentals Of Neural Networkmentioning

confidence: 99%

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Use of Neural Network to Characterize a Low Pressure Temperature Effect on Refractive Property of Silicon Nitride Film Deposited by PECVD

Kim

Hong

2004

IEEE Trans. Plasma Sci.

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Using a neural network, a refractive index (RI) of silicon nitride film was predicted as a function of process parameters, including radio frequency (RF) power, pressure, substrate temperature, and SiH 4 , NH 3 , and N 2 flow rates. The film was deposited by a plasma-enhanced chemical vapor deposition (PECVD) system. The PECVD process was characterized by a 2 6 1 fractional factorial experiment. Particular emphasis was placed on examining temperature effects at low pressure. Model prediction accuracy was optimized as a function of training factors. Predicted parameter effects were experimentally validated. Plots generated from an optimized model were used to qualitatively estimate deposition mechanisms. It is noticeable that under various plasma conditions, the RI varied little with the temperature. The temperature effect was extremely sensitive to the pressure level. Enhanced ion bombardment at high temperatures yielded a Si-rich film. Effect of each gas was little affected by the temperature. The SiH 4 flow rate played the most significant role in determining the RI at low pressure.Index Terms-Modeling, neural network, plasma-enhanced chemical vapor deposition (PECVD), silicon nitride (SiN) film.

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Section: Effects Of Nh Flow Rate and Substratesupporting

confidence: 73%

Section: Fundamentals Of Neural Networkmentioning

confidence: 99%

Use of Neural Network to Characterize a Low Pressure Temperature Effect on Refractive Property of Silicon Nitride Film Deposited by PECVD

Kim

Hong

2004

IEEE Trans. Plasma Sci.

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“…The major impact of the distance between electrodes, as demonstrated in figure 21, is on the uniformity of the deposited silicon nitride film. The electrode spacing variation has a minor diminishing effect on average deposition rate [64]; however, its effect on the local distribution of deposition rate is indispensable. According to Kushner [65] the deposition rate has generally been observed to decrease with increasing electrode separation.…”

Section: 35mentioning

confidence: 99%

Comprehensive simulation of the effects of process conditions on plasma enhanced chemical vapor deposition of silicon nitride

Bavafa

Ilati

Rashidian

2008

Semicond. Sci. Technol.

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A numerical model for the deposition of silicon nitride using silane and ammonia mixture in a radio frequency plasma reactor has been developed. Plasma enhanced chemical vapor deposition process is simulated by combined analysis for the glow discharge, fluid flow and chemical reactions. The main goal is to investigate the effect of variations of the process parameters on the deposition rate, and uniformity of the resulting layer. The approach used is based on the theoretical partial differential equation models, without any empirical approximation of the critical data being used. Owing to the fact that the relevant equations are highly nonlinear, the discretization method is of great importance. A finite boxes method is used to solve the partial differential equations (PDEs), and a Scharfetter-Gummel method is used to discretize the electron and ion flux terms.

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“…Machida improved the film step covering by adding bias [22]. Bierner et al studied the effect of the ratio of gaseous reactants on step coverage in the PECVD of silicon nitride [23]. Özkol et al deposited hydrogenated amorphous silicon (a-Si:H) films by PECVD and studied the relationship between the conformality of a-Si:H films and deposition temperature [24].…”

Section: Introductionmentioning

confidence: 99%

Optical Constant and Conformality Analysis of SiO2 Thin Films Deposited on Linear Array Microstructure Substrate by PECVD

et al. 2021

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SiO2 thin films are deposited by radio frequency (RF) plasma-enhanced chemical vapor deposition (PECVD) technique using SiH4 and N2O as precursor gases. The stoichiometry of SiO2 thin films is determined by the X-ray photoelectron spectroscopy (XPS), and the optical constant n and k are obtained by using variable angle spectroscopic ellipsometer (VASE) in the spectral range 380–1600 nm. The refractive index and extinction coefficient of the deposited SiO2 thin films at 500 nm are 1.464 and 0.0069, respectively. The deposition rate of SiO2 thin films is controlled by changing the reaction pressure. The effects of deposition rate, film thickness, and microstructure size on the conformality of SiO2 thin films are studied. The conformality of SiO2 thin films increases from 0.68 to 0.91, with the increase of deposition rate of the SiO2 thin film from 20.84 to 41.92 nm/min. The conformality of SiO2 thin films decreases with the increase of film thickness, and the higher the step height, the smaller the conformality of SiO2 thin films.

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Characterization of step coverage change in ultraviolet-transparent plasma enhanced chemical vapor deposition silicon nitride films

Cited by 13 publications

References 7 publications

Use of Neural Network to Characterize a Low Pressure Temperature Effect on Refractive Property of Silicon Nitride Film Deposited by PECVD

Use of Neural Network to Characterize a Low Pressure Temperature Effect on Refractive Property of Silicon Nitride Film Deposited by PECVD

Comprehensive simulation of the effects of process conditions on plasma enhanced chemical vapor deposition of silicon nitride

Optical Constant and Conformality Analysis of SiO2 Thin Films Deposited on Linear Array Microstructure Substrate by PECVD

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