Simulations of scanning electron microscopy imaging and charging of insulating structures

Grella, L.; Lorusso, Gian; Adler, D. L.

doi:10.1002/sca.4950250606

Cited by 17 publications

(17 citation statements)

References 11 publications

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“…More realistic simulations based on Monte Carlo simulation as described in (Davidson and Sullivan 1997;Grella et al 2001Grella et al , 2003Ko and Joy 2001;Ko et al 1998; Sullivan …”

Section: Discussionmentioning

confidence: 99%

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Scanning electron microscope charging effect model for chromium/quartz photolithography masks

2006

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We propose a new method for fitting a model of specimen charging to scanning electron microscope (SEM) images. Charging effects cause errors when one attempts to infer the size or shape of a specimen from an image. The goal of our method is to enable image analysis algorithms for measurement, segmentation, and three-dimensional (3-D) reconstruction that would otherwise fail on images containing charging effects. Our model is applied to images of chromium/quartz photolithography masks and may also work in the more general case of isolated metal islands on a flat insulating substrate. Unlike methods based on Monte Carlo simulation, our simulation method does not handle more general topographies or specimens composed entirely of an insulator; it is a crude approximation to the physical charging process described in more detail in Cazaux (1986) and Melchinger and Hofmann (1985), but can be fit with quantitative accuracy to real SEM images. We only consider changes in intensity and do not model charging-induced distortion of image coordinates. Our approach has the advantage over existing methods of enabling fast prediction of charging effects so it may be more practical for image analysis applications.

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“…More realistic simulations based on Monte Carlo simulation as described in (Davidson and Sullivan 1997;Grella et al 2001Grella et al , 2003Ko and Joy 2001;Ko et al 1998; Sullivan …”

Section: Discussionmentioning

confidence: 99%

“…More realistic simulations based on Monte Carlo simulation as described in (Davidson and Sullivan 1997;Grella et al 2001Grella et al , 2003 (1 − µ i c ) FIG. 11 Though our simulation contains some residual systematic error, it is significantly more accurate than an optimal piecewise constant fit to the image.…”

Section: Discussionmentioning

confidence: 99%

Scanning electron microscope charging effect model for chromium/quartz photolithography masks

2006

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“…7 For small FOV values, such that E inv exceeds E z , the surface voltage (1) is equal to ⌬V e and does not depend on the anode voltage or the applied field; the total electron yield in this case is one. 7 For small FOV values, such that E inv exceeds E z , the surface voltage (1) is equal to ⌬V e and does not depend on the anode voltage or the applied field; the total electron yield in this case is one.…”

Section: A Offset Chargementioning

confidence: 97%

“…7 If x is the linear length of the FOV and E z is the applied field (with E z Ͼ 0), then the surface voltage V so created by the offset charge can be described as 2,7 The offset voltage V so is the surface voltage created by the offset charge.…”

Section: A Offset Chargementioning

confidence: 99%

Three-dimensional simulation of top down scanning electron microscopy images

Grella¹,

Lorusso²,

Lee³

et al. 2004

Journal of Vacuum Science &Amp; Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena

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Efficient and accurate three-dimensional Poisson solver for surface problemsLow voltage scanning electron microscopy (SEM) metrology and inspection are performed by immersing the sample in an electric field; under this condition, when a scanning electron beam images a sample containing insulating features (like oxides and resist), a surface global charge builds up to offset the applied field and a transverse local field will form as a result of the scanning beam. The surface global charge is responsible for the voltage contrast and imaging properties, while local fields degrade image resolution. In this article we describe a simulation approach able to explain the imaging properties of charged surfaces and how resolution is affected by local fields. Using electron ray tracing in the column, the simulation follows both the emitted and primary electron trajectories outside the sample. In addition, Monte Carlo scattering simulation calculates the electron trajectory and charge deposition inside the sample. The resulting charge density is used to calculate the field inside and outside the sample by solving the Poisson equation with the proper boundary conditions. Ray tracing, Monte Carlo scattering simulation; and field equation are then integrated in a self-consistent fashion to form a simulation algorithm capable of explaining SEM imaging and charging. The simulation is applied to a variety of cases regarding both inspection and metrology. The results are compared with experiments. Furthermore a method to calculate surface charging will be given for both insulating surfaces and patterned insulating surfaces on a grounded substrate.

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“…For this reason, the virtual histology is simply produced by rendering images to have similar contrast to electron microscopy for comparison. It may be possible, however to generate more realistic electron microscopy images using a physically realistic electron microscopy simulator (Babin et al, 2010;Grella et al, 2003;Ophus, 2017) which may be used to train and test axon segmentation routines.…”

Section: Applications Beyond Diffusion Mrimentioning

confidence: 99%

Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation

Callaghan

Alexander

Zhang

et al. 2019

Lecture Notes in Computer Science

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This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-byone, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches.

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Simulations of scanning electron microscopy imaging and charging of insulating structures

Cited by 17 publications

References 11 publications

Scanning electron microscope charging effect model for chromium/quartz photolithography masks

Scanning electron microscope charging effect model for chromium/quartz photolithography masks

Three-dimensional simulation of top down scanning electron microscopy images

Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation

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