Refinement of Monte Carlo simulations of electron–specimen interaction in low-voltage SEM

Kieft, ER Erik; Bosch, Eric

doi:10.1088/0022-3727/41/21/215310

Cited by 88 publications

(84 citation statements)

References 35 publications

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“…10, there are three differences to be mentioned. First of all, our program does not use the Geant4 platform.…”

Section: Sem Simulatormentioning

confidence: 99%

“…It is designed from scratch for the purpose of solving problems involving large number of incident low energy particles and complex geometries, while using the refinements as described in Ref. 10. We consider three physical processes for the electron in our simulator.…”

Section: Sem Simulatormentioning

confidence: 99%

“…In our implementation, we follow Ref. 10 and tabulate the cumulative differential inverse inelastic mfps, where the differential is taken with respect to zero-momentum energy transfer,…”

Section: Sem Simulatormentioning

confidence: 99%

“…By calculating yield plots with the program of Ref. 10, we found that the significance of the Auger-effect on low energy secondary electron yields for silicon and PMMA is less than 1%. Finally, there is no cutoff energy for electrons and we keep tracking all electrons until they run out of energy, even when they have little or no chance of escaping the material.…”

Section: Sem Simulatormentioning

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%

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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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“…10, there are three differences to be mentioned. First of all, our program does not use the Geant4 platform.…”

Section: Sem Simulatormentioning

confidence: 99%

Section: Sem Simulatormentioning

confidence: 99%

“…In our implementation, we follow Ref. 10 and tabulate the cumulative differential inverse inelastic mfps, where the differential is taken with respect to zero-momentum energy transfer,…”

Section: Sem Simulatormentioning

confidence: 99%

Section: Sem Simulatormentioning

confidence: 99%

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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Extreme High‐Resolution (XHR) SEM Using a Beam Monochromator

Young¹,

Veen²,

Henstra³

et al. 2012

Low Voltage Electron Microscopy

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Influence of gold nanoparticles embedded in water on nanodosimetry for keV photon irradiation

2021

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For the past two decades, high-Z nanoparticles have been of high interest to improve the therapeutic outcomes of radiation therapy, especially for low-energy x-rays. Monte Carlo (MC) simulations have been used to evaluate the boost of dose deposition induced by Auger electrons near the nanoparticle surface, by calculating average energy deposition at the nanoscale. In this study, we propose to go beyond average quantities and quantify the stochastic nature of energy deposition at such a scale. We present results of probability density of the specific energy (restricted to ionization, excitation and electron attachment events) in cylindrical nanotargets of height and radius set at 10 nm. This quantity was evaluated for nanotargets located within 200 nm around 5-50 nm gold nanoparticles (GNPs), for 20-90 keV photon irradiation. Methods: This nanodosimetry study was based on the MC simulation MDM that allows tracking of electrons down to thermalization energy. We introduced a new quantity, namely the probability enhancement ratio (PER), by estimating the probability of imparting to nanotargets a restricted specific energy larger than a threshold z 0 (1, 10, and 20 kGy), normalized to the probability for pure water. The PER was calculated as a function of the distance between the nanotarget and the GNP surface. The threshold values were chosen in light of the biophysical model NanOx that predicts cell survival by calculating local lethal events based on the restricted specific energy and an effective local lethal function. z 0 then represents a threshold above which the nanotarget damages induce efficiently cell death. Results: Our calculations showed that the PER varied a lot with the GNP radius, the photon energy, z 0 and the distance of the GNP to the nanotarget. The highest PER was 95 when the nanotarget was located at 5 nm from the GNP surface, for a photon energy of 20 keV, a threshold of 20 kGy, and a GNP radius of 50 nm. This enhancement dramatically decreased with increasing GNP-nanotarget distances as it went below 1.5 for distances larger than 200 nm. Conclusions: The PER seems better adapted than the mean dose deposition to describe the formation of biological damages. The significant increase of the PER within 200 nm around the GNP suggests that severe damages could occur for biological nanotargets located near the GNP. These calculations will be used as an input of the biophysical model NanOx to convert PER into estimation of radiation-induced cell death enhanced by GNPs.

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Refinement of Monte Carlo simulations of electron–specimen interaction in low-voltage SEM

Cited by 88 publications

References 35 publications

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

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

Extreme High‐Resolution (XHR) SEM Using a Beam Monochromator

Influence of gold nanoparticles embedded in water on nanodosimetry for keV photon irradiation

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