Statistically accurate analysis of line width roughness based on discrete power spectrum

Hiraiwa, Atsushi; Nishida, Akio

doi:10.1117/12.846071

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…To extract the noise level of the CD-SEM, we have recently developed a method, 13 based on the PSD fitting method proposed by Hiraiwa et al 10,11,14,15 It consists of acquiring a large set of CD-SEM images (N Ã ) of a line in order to calculate a PSD (PSD) of the line LWR. The final PSD is the average of the N Ã calculated PSD.…”

Section: Lwr Characterizationmentioning

confidence: 99%

Plasma treatments to improve line-width roughness during gate patterning

Azarnouche

Pargon²,

Menguelti³

et al. 2013

J. Micro/Nanolith. MEMS MOEMS

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The major issue related to line width roughness (LWR) is the significant LWR of the photoresist patterns printed by 193-nm lithography that is partially transferred into the gate stack during the subsequent plasma etching steps. The strategy used today to overcome this issue is to apply postlithography treatments to reduce photoresist pattern LWR before transfer. In this article, we investigate the impact of various plasma treatments (HBr, H 2 , He, Ar) on the minimization of the LWR of dense and isolated photoresist patterns and its transfer during gate patterning. To do so, we use critical dimension scanning electron microscopy measurements combined with power spectrum density fitting method to extract unbiased LWR values and provide a spectral analysis of the LWR. We show that plasma treatments that lead to carbon redeposition from the gas phase on the resist pattern sidewalls are less efficient to reduce LWR than plasma treatments where the redeposition is limited. Among all plasma chemistries, H 2 plasmas seem very promising to decrease resist LWR in the whole spectral range, while maintaining square resist profiles. In addition, we show that all frequency roughness components are not equally transferred during gate patterning, and more particularly that the high frequency roughness components are lost.

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Section: Lwr Characterizationmentioning

confidence: 99%

Plasma treatments to improve line-width roughness during gate patterning

Azarnouche

Pargon²,

Menguelti³

et al. 2013

J. Micro/Nanolith. MEMS MOEMS

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“…a) LWR consists of net and image-noise-induced components, w (y) and w noise ðyÞ, which are statistically independent. b) Similarly to that in our previous works, [19][20][21] w (y) has an autocorrelation function (ACF) ðyÞ given in…”

Section: Methodsmentioning

confidence: 85%

“…z e ið2=NÞÀÁy= , and R is the ratio of the variance varð'Þ of w noise ðyÞ to varðwÞ, i.e., R varð'Þ= varðwÞ. The fitting is carried out by minimizing the parameter " defined in 20,21) "…”

Section: Methodsmentioning

confidence: 99%

“…19,20) The accuracy of estimation by this method largely depends on the levels of statistical noise and scanning-electron-microscope (SEM) image noise, as well as on measurement and analysis conditions. 21) As one of systematic studies to provide guidelines on setting up measurement and analysis conditions for accurate analyses, we separately investigated the effect of the statistical noise on the accuracy of analyses using the PSD fitting method in another work of ours. 22) On the basis of the work, in this study, we clarify the effect of another type of noise, i.e., image noise, in relation to measurement and analysis conditions.…”

Section: Introductionmentioning

confidence: 99%

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Image-Noise Effect on Discrete Power Spectrum of Line-Edge and Line-Width Roughness

Hiraiwa

Nishida

2011

Jpn. J. Appl. Phys.

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The authors investigated the effect of scanning-electron-microscope image noise on the accuracy of line-edge-roughness and line-width-roughness (LER/LWR) statistics extracted from power spectral densities (PSDs). To do this, they numerically prepared pseudo-experimental PSDs of LWR using the Monte Carlo (MC) method. The estimation error η decreased with the total number N ALL of width data points in the same way as that observed in the absence of the image noise. η first increased gradually with the image-noise intensity R but markedly when R went beyond the threshold value R th determined by the ratio of the sampling interval Δy to the correlation length ξ. The PSDs with these R th's had the same maximum-to-minimum ratio γ (= 10 in this study). The authors approximated η by B N ALL -3/4(Δy/ξ)-3/8 [1+g(R,Δy/ξ,γ)], where B is 49. They also empirically determined the functional form of g(R,Δy/ξ,γ). Because these functions well fitted massive MC simulation results, they provide guidelines for setting up analysis conditions for securing arbitrarily prescribed accuracy.

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“…One of the analysis procedures is to employ image processing techniques by which the boundaries of features are detected and compute the CD and LER from the boundaries. [5][6][7][8][9][10][11][12][13][14] Since SEM images tend to be noisy, it is essential to reduce the noise level before the boundary (edge) detection is carried out. The noise filtering has a direct effect on the accuracy of boundary detection, and therefore, it is critical to employ a noise filter optimized for the detection of feature boundaries in SEM images.…”

Section: Introductionmentioning

confidence: 99%

Designing an anisotropic noise filter for measuring critical dimension and line edge roughness from scanning electron microscope images

Lee

2018

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

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The scanning electron microscope (SEM) is often employed in inspecting patterns transferred through a lithographic process. A typical inspection is to measure the critical dimension (CD) and line edge roughness (LER) of each feature in a transferred pattern. Such inspection may be done by utilizing image processing techniques to detect the boundaries of a feature. Since SEM images tend to include a substantial level of noise, a proper reduction of noise is essential before the subsequent process of edge detection. In a previous study, a method of designing an isotropic Gaussian filter adaptive to the noise level was developed. However, its performance for relatively small features was not so good as for large features, especially in the case of LER. The main objective of this study is to improve the design method such that the accuracy of the measured CD and LER is not deteriorated substantially as the feature size decreases. The new design method allows a Gaussian filter to be anisotropic for the better adaptability to the signal and noise, both of which show a substantial level of directional correlation. The cutoff frequency for the direction normal to features is determined to include most of the signal components, and the cutoff frequency in the other direction is set to balance the signal and noise components to be included. This procedure enables a systematic and easy design of the filter. Also, the method of estimating the noise has been modified for higher accuracy. The performance of the new design method has been thoroughly analyzed using the reference images for which feature boundaries are known. It has been shown that the anisotropic filter designed by the proposed method performs better than the isotropic filter from the viewpoint of CD, LER, and power spectral density accuracy.

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Statistically accurate analysis of line width roughness based on discrete power spectrum

Cited by 12 publications

References 11 publications

Plasma treatments to improve line-width roughness during gate patterning

Plasma treatments to improve line-width roughness during gate patterning

Image-Noise Effect on Discrete Power Spectrum of Line-Edge and Line-Width Roughness

Designing an anisotropic noise filter for measuring critical dimension and line edge roughness from scanning electron microscope images

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