Unbiased line width roughness measurements with critical dimension scanning electron microscopy and critical dimension atomic force microscopy

Azarnouche, L.; Pargon, E.; Menguelti, K.; Fouchier, M.; Fuard, D.; Gouraud, P.; Verove, C.; Joubert, O.

doi:10.1063/1.4705509

Cited by 47 publications

(49 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of integration frames should be kept as low as possible to reduce the effect of resist shrinkage. 8,[10][11][12] However, detection algorithms, such as thresholdbased detectors or Canny-edge detection filters, often do not find the edge or find too many edges in such noisy data. Working with low-noise images has two problems: they take a long time to accumulate and there is a risk of resist shrinkage.…”

Section: Line Edge Determinationmentioning

confidence: 99%

“…Although the edge displacements already give a direct measurement of the roughness, the LER is best analyzed by the power spectral density (PSD). [5][6][7][8][9] There are a couple of problems related to this type of determination. First of all, there are statistical and systematic errors because the actual PSD is approximated by sampling the edge displacements of a pattern with a finite number of measurement intervals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of line edge roughness in low dose top-down scanning electron microscopy images

Verduin

Kruit

Hagen

2014

Metrology, Inspection, and Process Control for Microlithography XXVIII

View full text Add to dashboard Cite

Abstract. We investigated the off-line metrology for line edge roughness (LER) determination by using the discrete power spectral density (PSD). The study specifically addresses low-dose scanning electron microscopy (SEM) images in order to reduce the acquisition time and the risk of resist shrinkage. The first attempts are based on optimized elliptic filtering of noisy experimental SEM images, where we use threshold-based peak detection to determine the edge displacements. The effect of transversal and longitudinal filterings cannot be ignored, even when considering an optimized filter strength. We subsequently developed a method to detect the edge displacements without the use of a filter and thus avoiding biasing. This makes it possible to study how much image noise is acceptable and still determine the LER. The idea is to generate random images of line edges using the model of Palasantzas and the algorithm of Thorsos. We study the simulated PSDs as a function of the number of line edges and report on the convergence of the parameters (LER, correlation length, and roughness exponent) by fitting the Palasantzas model extended with a white noise term. This study demonstrates that a very noisy image with 12 line edges and about 2 electrons per pixel on average (charge density ≈10 μC) already produces an estimation for LER with a relative error (one-sigma) of about 10%. Furthermore, increasing the dose beyond 20 electrons per pixel does not significantly improve the LER determination.

show abstract

Section: Line Edge Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of line edge roughness in low dose top-down scanning electron microscopy images

Verduin

Kruit

Hagen

2014

Metrology, Inspection, and Process Control for Microlithography XXVIII

View full text Add to dashboard Cite

show abstract

“…Not only is PMMA a different material, it also comes, contrary to pure silicon lines, with a risk of resist shrinkage caused by the electron beam. 5 Although it is not addressed in the work of Lawson et al, this risk can be reduced, for example, by lowering the beam voltage and total electron dose, i.e. reduce the number of integration frames.…”

Section: Introductionmentioning

confidence: 99%

“…reduce the number of integration frames. 1,5 We would like to extend the study of Refs. 2 and 3 and examine the relation between SWR and top-down measured LER by varying the parameters of a non-Gaussian roughness model.…”

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%

See 1 more Smart Citation

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

et al. 2015

View full text Add to dashboard Cite

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.

show abstract

Patterning Si at the 1 nm Length Scale with Aberration‐Corrected Electron‐Beam Lithography: Tuning of Plasmonic Properties by Design

Manfrinato

Camino

Stein

et al. 2019

Adv Funct Materials

View full text Add to dashboard Cite

Patterning of materials at single nanometer resolution allows engineering of quantum confinement effects, as these effects are significant at these length scales, and yields direct control over electro‐optical properties. Silicon is by far the most important material in electronics, and the ability to fabricate Si‐based devices of the smallest dimensions for novel device engineering is highly desirable. The work presented here uses aberration‐corrected electron‐beam lithography combined with dry reactive ion etching to achieve both: patterning of 1 nm features and surface and volume plasmon engineering in Si. The nanofabrication technique employed here produces nanowires with a line edge roughness (LER) of 1 nm (3σ). In addition, this work demonstrates tuning of the Si volume plasmon energy by 1.2 eV from the bulk value, which is one order of magnitude higher than previous attempts of volume plasmon engineering using lithographic methods.

show abstract

Unbiased line width roughness measurements with critical dimension scanning electron microscopy and critical dimension atomic force microscopy

Abstract: International audienc

Cited by 47 publications

References 32 publications

Determination of line edge roughness in low dose top-down scanning electron microscopy images

Determination of line edge roughness in low dose top-down scanning electron microscopy images

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

Patterning Si at the 1 nm Length Scale with Aberration‐Corrected Electron‐Beam Lithography: Tuning of Plasmonic Properties by Design

Contact Info

Product

Resources

About