Understanding the relationship between true and measured resist feature critical dimension and line edge roughness using a detailed scanning electron microscopy simulator

Lawson, Richard A.; Henderson, Clifford L.

doi:10.1116/1.3517717

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…4 Seeking to bridge the gap, we have developed a Monte Carlo electron scattering simulation tool that can create SEM images of 3D features with arbitrary geometry in order to probe the effect of roughness on SEM measurements. 5 Synthetic sidewalls are created to systematically vary roughness parameters and see the effect on SEM measured LER. Going beyond synthetic sidewalls, we have combined this SEM simulation with an earlier 3D stochastic resist model 6 to try to connect the resist model output shown in Figure 1 to an actual SEM equivalent line edge profile.…”

Section: Introductionmentioning

confidence: 99%

Investigating SEM metrology effects using a detailed SEM simulation and stochastic resist model

Lawson

Henderson

2015

SPIE Proceedings

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A Monte Carlo electron scattering simulation tool that can create SEM images of 3D features with arbitrary geometry has been developed. This is combined with both a stochastic resist model and synthetic 3D features to probe the effect of the effect of roughness on SEM measurements. Sidewall roughness makes it difficult to precisely identify the true feature width of a line because the roughness increases the SEM signal non-proportionally to the amount of material with which it is interacting. LER generally under predicts sidewall surface roughness because the SEM has an averaging effect as the electron beam interacts with a volume of material. LER becomes a better measure of surface roughness as the correlation length of the surface roughness increases. Decreasing film thickness causes a decrease in the linewidth and increase in LER measured by SEM, especially for features 35 nm thick and below. This occurs even if the true 3D feature width and roughness is approximately constant, meaning that the apparent change in linewidth and LER is a metrology effect. Threshold based estimations of line edges are difficult because the threshold choice that best matches the true feature width changes with the feature geometry. Model based library fits of linescans do not appear to provide a solution because sidewall roughness and sidewall angle have similar effects on the linescan meaning no unique linescan likely exists.Keywords: photoresists, scanning electron microscopy, electron scattering, sidewall roughness, line edge roughness, film thickness effects on LER, sidewall angle, stochastic photoresist model INTRODUCTIONScanning electron microscopes (SEMs) have long been and will likely remain the metrology tool of choice due to their ease of use, high resolution, and ability to easily image at a range of length scales. Despite often being treated as such, the image produced by an SEM is not identical to an optical image. This is because the SEM works by shooting a beam of electrons into a sample and then measuring the number of lower energy secondary electrons emitted by the sample for that incident location. This electron-material interaction means that there are certain biases or effects associated with the measurement that must be accounted for to correctly interpret the SEM image. One commonly known effect is the fact that the SEM signal is more sensitive to topography; this means that the signal is higher at the edges of features than in the middle. This fact is used to measure the width of features by identifying the edges where the signal is highest. Like many things in the semiconductor industry, SEM metrology of patterned features has become more difficult as the features sizes have shrank. The current length scales of features can potentially cause a whole new series of metrology effects that must be accounted for to correctly interpret the SEM images.One of the major issues is that the roughness length scale of current features is on the order of the feature size itself. This means there can be significant errors...

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Section: Introductionmentioning

confidence: 99%

Investigating SEM metrology effects using a detailed SEM simulation and stochastic resist model

Lawson

Henderson

2015

SPIE Proceedings

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“…In order to compare, we must know the effective correlation length ξ 3D for the SWR of PMMA in the mesoscopic roughness model of Lawson et al Unfortunately, the parameters that they have used in the mesoscopic model for PMMA are not mentioned in Ref. 3 and a direct comparison can therefore not be made. In any case, the result of Fig.…”

Section: Simulation Of Rough Linesmentioning

confidence: 99%

“…Then there are programs based on dielectric function theory. We mention the programs of Kieft et al 10 (developed at FEI company), Li et al 2 and Lawson et al 3 Unfortunately, most of these programs are not available to us and the programs that we do have, are not designed to manage the complex threedimensional geometrical features of this study. The program of Kieft et al has drawn our special attention for two important reasons.…”

Section: Introductionmentioning

confidence: 99%

“…The studies that we found in literature indicate that the true SWR is larger than the measured LER in a top-down SEM image. 2,3 We have a few remarks with respect to these studies. In the study of Li et al, 2 the focus is on pure polycrystalline silicon lines with a Gaussian roughness model for the sidewalls.…”

Section: Introductionmentioning

confidence: 99%

“…The roughness of a resist feature, however, is typically characterized by more than just the standard deviation of a Gaussian-like distribution and involves additional parameters, such as correlation length and a roughness exponent. 1,[4][5][6] The study of Lawson et al 3 is different for two reasons. In the first place, Lawson et al have used a more sophisticated roughness model.…”

Section: Introductionmentioning

confidence: 99%

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The effect of sidewall roughness on line edge roughness in top-down scanning electron microscopy images

et al. 2015

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We have investigated in a numerical study the determination of sidewall roughness (SWR) from topdown scanning electron microscopy (SEM) images. In a typical metrology application, top-down SEM images are acquired in a (critical-dimension) SEM and the roughness is analyzed. However, the true size, shape and roughness characteristics of resist features are not fully investigated in the analysis of top-down SEM images. In reality, rough resist features are complex three-dimensional structures and the characterization naturally extends to the analysis of SWR.In this study we randomly generate images of rough lines and spaces, where the lines are made of PMMA on a silicon substrate. The lines that we study have a length of 2 µm, a width of 32 nm and a height of 32 nm. The SWR is modeled by using the power spectral density (PSD) function of Palasantzas, which characterizes roughness by the standard deviation σ, correlation length ξ and roughness exponent α. The actual roughness is generated by application of the method of Thorsos in two dimensions. The images are constructed by using a home-built program for simulating electron-specimen interactions. The program that we have developed is optimized for complex arbitrary geometries and large number of incident low energy primary electrons by using multicore CPUs and GPUs. The program uses the dielectric function model for inelastic scattering events and has an implementation specifically for low energy electrons. A satisfactory comparison is made between the secondary electron yields from the home-built program and another program found in literature. In order to reduce the risk of shrinkage, we use a beam energy of 300 eV and a spot size of 3 nm. Each pixel is exposed with 20 electrons on average (≈ 276 µC/cm 2 ), following the Poisson distribution to account for illumination shot noise. We have assumed that the detection of electrons is perfect and does not introduce additional noise.We measure line edge roughness (LER) in simulated top-down SEM images of randomly generated rough lines by using PSD analysis. The measurements are then compared to the actual SWR that was used to generate the rough lines. We conclude that the bias in the determination of SWR is a non-linear function of the correlation length ξ 3D of the actual SWR. The measured correlation length ξ 2D shows a linear trend with the correlation length ξ 3D of the SWR. From another simulation run, we conclude that the relation between measured LER in the top-down image and the standard deviation σ 3D of the SWR is linearly biased. We see that the amount of bias relates to the correlation length ξ 3D of the SWR: The bias in the determination of SWR from top-down images increases for decreasing correlation length ξ 3D of the actual SWR. The results of this study, with respect to the metrology of rough resist features, touches upon the reliability and comparability of roughness characterization in top-down images.

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Overview of materials and processes for lithography

Lawson

Robinson

2016

Materials and Processes for Next Generation Lithography

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Understanding the relationship between true and measured resist feature critical dimension and line edge roughness using a detailed scanning electron microscopy simulator

Cited by 11 publications

References 19 publications

Investigating SEM metrology effects using a detailed SEM simulation and stochastic resist model

Investigating SEM metrology effects using a detailed SEM simulation and stochastic resist model

The effect of sidewall roughness on line edge roughness in top-down scanning electron microscopy images

Overview of materials and processes for lithography

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