Klein 2012

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Chapter6

TSEM: A Review of ScanningElectron Microscopy inTransmission Mode and ItsApplications

Tobias Klein∗, Egbert Buhr∗, and Carl Georg Frase∗

Contents 1. Introduction 298I Technique 2992. Common Electron Microscopy Techniques 299

2.1. Transmission Electron Microscopy 3002.2. Scanning Electron Microscopy 3012.3. Scanning Transmission Electron Microscopy 302

3. TSEM Signal Generation 3033.1. Detection of Transmitted Electrons 3033.2. Physical Background: Electron Scattering

and Diffraction 3073.3. Contrast Mechanisms 3103.4. Monte Carlo Simulation of TSEM Signals 314

4. TSEM Compared with Common Electron MicroscopyTechniques 3204.1. Resolution 3204.2. Contrast 3234.3. Energy-Dispersive X-Ray Spectroscopy 3244.4. Sample Preparation and Throughput 324

II Applications 3265. Traceable Dimensional Measurements

of Nanostructures 3265.1. Calibration of an SEM 3265.2. Mask Metrology 3285.3. Nanoparticle Size Measurement 331

6. Characterization of Different Material Classes 340

∗ Physikalisch-Technische Bundesanstalt, Bundesallee 100, 38116 Braunschweig, Germany

Advances in Imaging and Electron Physics, Volume 171, ISSN 1076-5670, DOI: 10.1016/B978-0-12-394297-5.00006-4.Copyright c© 2012 Elsevier Inc. All rights reserved.

297

http://dx.doi.org/10.1016/B978-0-12-394297-5.00006-4


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298 Tobias Klein, Egbert Buhr, and Carl Georg Frase

6.1. Biological Samples 3406.2. Polymers 3416.3. Semiconductor 3416.4. Material Science 343

7. Special Imaging Modes 3447.1. TSEM in Liquids 3447.2. Electron Energy-Loss Spectroscopy 3457.3. Tomography 3457.4. Visualization of Electric Fields 346

8. Conclusion 346List of Abbreviations 347References 348

1. INTRODUCTION

The history of electron microscopy begins with Hans Busch (1926, 1927),who introduced electron optics, and continues with Ernst Ruska, whowas awarded the Nobel Prize for building the first transmission elec-tron microscope (TEM) (Knoll and Ruska, 1932), which led to commercialinstruments as early as 1939 (Wolpers, 1991). In a TEM, the sample isilluminated with a broad immobile beam. The principle of scanning afocused electron beam across the sample was introduced by ManfredVon Ardenne (1938a,b) and put into practice by Charles W. Oatley, whosework led to the introduction of the first commercial scanning electronmicroscope (SEM), the Cambridge “Stereoscan”, in 1965 (Oatley et al.,1966). It featured detectors for secondary and backscattered electrons,which represent the standard equipment of SEMs to this day. Paralleldevelopment in Japan led to the launch of a second commercial SEM, theJEOL “JSM”, only six months later (Fujita, 1986).

The JSM was additionally equipped with a transmission detector thatcould be placed underneath the sample to detect primary electrons thattransmit through the specimen. Consequently, two employees of JEOLwere the first to discuss details of this mode, which today we call TSEM1

(Kimoto and Hashimoto, 1968).Initially, there was little interest in transmission work with the SEM

because, at that time, TEMs had already been available for more than 25years and had become capable of nanometer resolution. The transmis-sion mode of an SEM did not seem to be a promising alternative sinceit allowed resolutions of only about 10 nm (Kimoto and Hashimoto, 1968).

1 In the literature, diverse abbreviations for SEM in transmission mode are used: TSEM, STEM, STEM-in-SEM,STEM/SEM, STEM-SEM, LVSTEM (for low-voltage STEM), and others. Throughout this text we use TSEM,which was most likely introduced by Postek et al. (1989). In analogy to a STEM (a TEM used in scanning mode)we think TSEM is instructive for an SEM operated in transmission mode. We do not recommend the usageof STEM since this abbreviation is mainly used to denote dedicated high-voltage instruments. Unfortunately,most SEM manufacturers use this delusive term. STEM-in-SEM is unambiguous since it explicitly names whatis done, but its length may be a little awkward.


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TSEM: A Review of SEM in Transmission Mode 299

Today, things have changed. Due to improved electron opticsespecially designed for low-energy electrons and the use of field emissionguns, resolutions down to 0.4 nm have been demonstrated using TSEM(Van Ngo et al., 2007). At the same time, the demand for high-resolutionimaging is growing due to technological progress and ongoing miniatur-ization in semiconductor industry, nanotechnology, and material science.In principle, TEM is capable of fulfilling this demand, whereas in prac-tice there are obstacles. The purchase and operation of a TEM are costlyand analyzing samples takes a long time. If the highest resolution is notneeded, TSEM is an inexpensive and fast alternative that considerablyincreases sample throughput and reduces the costs per sample.

TSEM is well suited for nanotechnological applications since the opti-mal sample thickness for TSEM ranges from some nanometers to somehundred nanometers. In this range, TSEM yields high-contrast imageseven for low-Z materials due to strong electron scattering. In addition, itprovides higher lateral resolution compared with common SEM imagingmodes, since it is not confined by the size of the interaction volume.

Consequently, there is rising interest in TSEM and today all SEM man-ufacturers offer the possibility of upgrading their SEMs with transmissiondetectors. Although still on a small scale, the amount of publications ofwork accomplished with the help of TSEM has been steadily increasingsince the turn of the millennium. Many of these publications do not explic-itly deal with TSEM but use it as a given tool to accomplish the task athand.

This contribution summarizes the basics of the TSEM technique (Part I)and presents an overview of the fields in which it has been applied to date(Part II). Part I begins with a short introduction of the common electronmicroscopy techniques. Subsequently, the configuration and the imple-mentation of the transmission detector are introduced. Electron scatteringis briefly described, which is the basis of the different contrast mecha-nisms and may be simulated using Monte Carlo methods. In the followingsection, we compare some aspects of TSEM with the common electronmicroscopy techniques. The overview of TSEM applications in Part IIstarts with traceable dimensional measurements before the characteriza-tion of various material classes is described and special imaging modesare presented.

PART I: TECHNIQUE

2. COMMON ELECTRONMICROSCOPY TECHNIQUES

This section introduces the well-known and commonly used electronmicroscopy techniques. They share many basic principles with TSEM as


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discussed in Section 3 on TSEM signal generation. A comparison of someaspects between the common techniques and TSEM follows in Section 4.

2.1. Transmission Electron Microscopy

The design of a transmission electron microscope is shown on the left sideof Figure 1. It basically resembles that of a transmission light microscope.The electrons emitted by the source are accelerated (typically by 200 kV)on their way to the sample. Condenser optics is used to achieve a spatiallyuniform illumination on the electron-transparent specimen. After passingthrough the sample, the electrons are collected and imaged on a projectionscreen by means of electron optics. A scintillator converts the impingingelectrons to light pulses that may also be detected by a charge-coupleddevice (CCD) for direct image recording using a computer.

The electrons are scattered, diffracted, and possibly absorbed by thespecimen, depending on different sample properties, such as mass, thick-ness, elemental composition, and crystallinity. The image is generatedby detecting electrons that are deflected within certain angular ranges,which are determined by the aperture of the imaging lens. Dark-field (DF)imaging uses only the deflected electrons for image formation, whereas inbright-field (BF) mode the beam of undeflected electrons is registered. Inhigh-resolution TEM mainly phase contrast is exploited. The sample actsas a phase object that distorts the wave front of the impinging electron

Source

Specimen

ApertureLens

Scanningcoils

Screen : Source

Detector

FIGURE 1 Schematic of the ray path in a TEM (left) and a STEM (right) demonstratingthe principle of reciprocity (Crewe and Wall, 1970). Image reprinted with kind permissionfrom Elsevier.


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wave. Under Scherzer defocus conditions, phase contrast is transformedinto visible amplitude contrast (Scherzer, 1949).

2.2. Scanning Electron Microscopy

In an SEM, the electron beam is focused on the sample and scanned ina raster. As a consequence of multiple scattering of beam electrons inthe specimen the interaction volume evolves, which can exceed the sizeof the electron probe considerably (Figure 2). The interaction of the pri-mary electrons with the sample leads to a variety of signals that maybe exploited to gain information. Because the detected signal originatesfrom electrons that emerge from the surface, there is no restriction to thinelectron-transparent samples. Due to the interaction volume, electronsmay also be excited far away from the scan position, leading to back-ground noise and a decrease in image quality. The intensity of the detectedsignal is converted to a grey-scale value, which is attributed to the currentscan position. In this way, a grey-scale image evolves pixel by pixel.

A portion of the primary electrons is scattered back and escapesthe sample. The detection of these backscattered electrons (BSEs) leads

Objective lens

X-ray

BSE

BSEInteractionvolume

SE escapedepth

SEdetector

Region of BSEproduction

Electronbeam

SE3SE1SE2

FIGURE 2 Overview of the various signal types that may be exploited in SEM imaging.The resolution is often limited by the size of the interaction volume, which forms insidethe specimen due to multiple electron scattering.


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to intense material contrast because the backscatter efficiency stronglydepends on the atomic number of the element.

Imaging with secondary electrons (SEs), which exhibit energies below50 eV by definition, is widely used. The number of SEs released dependson the geometry of the sample and on the angle of incidence of the elec-tron beam. In combination with a large depth of focus due to small coneangles of the beam, the well-known quasi three-dimensional (3D) imageimpression is obtained.

There are several excitation processes for SE. The most important one isthe excitation of plasmons (oscillations of the electron gas) by high-energyelectrons and their subsequent decay, which may lead to the generationof SEs. SEs are generated in the whole interaction volume by primary andbackscattered electrons, but due to their low energies only those gener-ated close to the surface are able to emit from the sample. SEs, whichare excited by primary electrons, the so-called SE1, are highly localized(see Figure 2) and retain information about the sample area that is hit bythe beam spot. Because BSEs emerge from inside the interaction volume,their travel range is not confined to the beam spot and the SE2 excitedby them originate from a relatively wide emission zone. If BSEs hit thewalls of the vacuum chamber or the electron column, SE3 are produced,which contribute to the background noise, thereby decreasing contrast.If the electron beam reaches an edge on the sample, SE emission is nolonger confined to the horizontal sample surface but may also take placeat the vertical edge. This so-called blooming effect may obscure the exactposition of the edge.

After impact ionization, the resulting vacancy in the shell may be filledby an electron from a higher energy level, thereby possibly emitting anX-ray photon specifically for the atomic element. These X-rays can be ana-lyzed by energy-dispersive X-ray spectroscopy (EDX) to gain informationabout the elemental composition of the sample.

2.3. Scanning Transmission Electron Microscopy

In a scanning transmission electron microscope (STEM), the scanningprinciple is combined with high-voltage operation. The electrons trans-mitted through the sample are detected, depending on their angle ofdeflection or their energy loss. In addition to BF and DF imaging thehigh-angle annular dark-field (HAADF) method is often used becauseit is highly element specific. Regarding energy-dependent detection, theallocation and concentration of elements can be determined by electronenergy-loss spectroscopy because the distribution of energy–losses ofprimary electrons is characteristic for specific elements.

When Crewe and his coworkers put STEM into practice at the end ofthe 1960s (Crewe and Wall, 1970; Crewe et al., 1970, 1968), the electron


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optics theory of TEM imaging was already well developed. Cowley (1969)realized that the so-called principle of reciprocity (Pogany and Turner,1968; Von Laue, 1935) allows the interpretation of diffraction phenom-ena in a STEM. The principle of reciprocity states that if the detectoracceptance angle of a STEM equals the angle of incidence of a TEM,then exactly the same micrograph can be obtained with the two instru-ments (Humphreys, 1981). This is because the ray path in a STEM maybe regarded as being the same as in a TEM except for being reversed (seeFigure 1). Although the conditions usually do not match exactly, manySTEM imaging modes have equivalent and well-known TEM counter-parts.

As described in the next section, TSEM and STEM share the same basicconcept. Thus the principle of reciprocity also holds for diffraction effectsin TSEM imaging.

3. TSEM SIGNAL GENERATION

3.1. Detection of Transmitted Electrons

3.1.1. Detector Configuration and Imaging Modes

TSEM is implemented into standard SEMs using their acceleration volt-ages usually not exceeding 30 kV. The electron beam focused by theprobe-forming lens is scanned across the sample by means of scanningcoils, and transmitted electrons (TEs) are registered by a transmissiondetector underneath the sample.

The beam electrons interact with the sample in elastic and inelasticscattering processes. If the sample is crystalline, diffraction from its latticeplanes must also be considered. Consequently, the energetic and angulardistribution of the beam electrons is changed, which may be exploited togenerate image contrast.

Since the energy loss of primary electrons is usually small comparedwith their initial energy, mainly the angular distribution of the scatteredelectrons is exploited in TSEM imaging by limiting the angular detectionrange. Three ranges are differentiated (Figure 3), although not all of themare necessarily implemented. In the BF mode only the central beam andbarely deflected electrons are detected, whereas the electrons scatteredto higher angles are blocked by a circular aperture. In the DF mode theprimary beam is blocked and only electrons scattered to medium anglescontribute to the image. This mode may be realized either by annular aper-tures or by separate detectors. If only electrons scattered to high anglesare detected, the resulting signal is usually not influenced by diffractioneffects which are mostly confined to small diffraction angles. This is whyHAADF detectors are sometimes implemented.


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Objective lens

Electron beam

Sample

BFDFHAADF

FIGURE 3 In transmission imaging, detector segments for bright-field (BF) anddark-field imaging (DF) are usually provided, sometimes accompanied by segments forthe high-angle annular dark field (HAADF) mode.

(b)(a)

100 nm 100 nm

FIGURE 4 Micrographs of silica particles in the hole of a lacy carbon film taken(a) in bright-field and (b) in dark-field mode (Buhr et al., 2009). Images reprinted withkind permission from IOP Publishing.

In BF imaging, sample areas that scatter electrons strongly have a darkappearance (Figure 4a). The image contrast may be adjusted by varyingthe acceptance angle of the BF detector, which determines the ratio ofelectrons that are detected and omitted, respectively.


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The DF mode is characterized by high contrast since only scatteredelectrons contribute to the image. The majority of electrons belong to theunscattered beam, which is not detected. Therefore, DF imaging is sensi-tive to small variations in sample properties. The obtained micrographsare often complementary to BF images (Figure 4b).

In dedicated STEMs the detector acceptance angle can be adjusted ver-satilely by means of a projection lens. Since there usually are no projectionlenses in TSEMs the adjustment is somewhat constrained. Main changesare made by changing the geometry of the detector or using apertures ofdifferent sizes. Smaller adjustments are possible by varying the distancebetween the sample and the detector. The best choice of the acceptanceangle is governed by the sample and the desired type of information.

3.1.2. Implementation

The simplest way to enable transmission imaging without modifyingthe SEM is to use a sample holder that can convert the transmittedelectrons to SEs which are subsequently registered using the standardEverhart–Thornley SE detector. Crawford and Liley (1970) used a polishedaluminum block for conversion that was slanted and oriented towards theSE detector. By moving the block it was possible to optimize image con-trast and to choose for BF or DF imaging. Nemanic and Everhart (1973)used a specimen stub for conversion. An aperture was mounted abovethe stub to improve contrast. A specimen stub for DF imaging was intro-duced by McKinney and Hough (1976). The specimen was placed abovethe gold-sputtered stub with a diametral line of carbon. While the scat-tered electrons impinging on the gold led to the generation of SEs thatwere detected, the unscattered electrons were effectively absorbed by thecarbon.

The elaborate conversion sample holder of Oho et al. (1987b) con-sisted of three surfaces slanted at different angels. With this design theBSEs produced when the transmitted electrons hit the uppermost sur-face could be also exploited in the conversion process. Vanderlinde (2002)improved the achievable resolution of conversion imaging by introduc-ing a graphite collimator to effectively absorb electrons scattered to highangles.

The main disadvantage of the conversion approach is an inferiorsignal-to-noise ratio (SNR) compared with dedicated transmission detec-tors, which are usually based either on solid-state detectors or a scintillatorcoupled to a photomultiplier via a light tube. The latter approach wasused in the JEOL JSM, the first commercial SEM offering TSEM imag-ing (Kimoto and Hashimoto, 1968). Today scintillation detectors may befound in SEMs from Hitachi and JEOL.


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Homemade transmission detectors using scintillators have also beendescribed: Swift et al. (1969) upgraded a Cambridge Stereoscan by cou-pling the scintillator for transmission measurements via a light pipe tothe photomultiplier of the Everhart–Thornley detector. Wells and Bre-mer (1970) used a similar approach with a turret that enabled them tochange between up to four detectors coupled to the Stereoscan’s photo-multiplier, one of which was a transmission detector. In the arrangementof Woolf et al. (1972) both the sample and the interchangeable aperturecould be moved independently by means of two separate xy-translationstages. Krzyzanek et al. (2004) installed a Faraday cup at the central pointof a scintillator enabling DF transmission imaging simultaneous to beamcurrent measurements with the aim of thickness determination.

Although scintillation detectors may be faster and may exhibit betterSNRs, most commercial transmission detectors use solid-state detectorsin various configurations. They are smaller, more flexible, and thus eas-ier to implement, especially if several detector areas for different imagingmodes are to be combined. Vendors of SEM accessories also distributetransmission detectors that can be incorporated into most current SEMs.One example is the transmission detector from K.E. Development thatwas applied for nanoparticle characterization (see Section 5.3). It con-sists of five solid-state detectors (Figure 5). The four detectors on topare used for DF imaging. The detector area for BF imaging is placedunderneath a small aperture. Since the geometry of the aperture cannot be

Specimen

Dark-fielddetector

Bright-fielddetector

FIGURE 5 Scheme of a typical solid-state transmission detector capable of bright-fieldand dark-field imaging. Image courtesy of Carl Zeiss NTS GmbH.


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changed, the acceptance angle must be adjusted by varying the distancebetween sample and detector. In principle, annular BSE detectors mayalso be used as transmission detectors if they are mounted underneath thesample.

To the authors’ knowledge the FEI transmission detector is theonly commercially available one that offers extra detector segments forHAADF imaging (Pfaff et al., 2010; Volkenandt et al., 2010; Young et al.,2008). The highest resolutions so far have been demonstrated using aspecial SEM from Hitachi in which the sample is located inside the elec-tron column (Tuysuz et al., 2008; Van Ngo et al., 2007). One disadvantageis that this approach imposes similar restrictions on the sample size ashigh-voltage instruments.

3.2. Physical Background: Electron Scattering and Diffraction

Scattering and diffraction are discussed briefly in this subsection sincethey are the physical origin of contrast generation in the TSEM mode. Thebooks by Reimer (1998, 2008) cover many aspects of electron scatteringrelevant to electron microscopy, whereas a thorough treatment is given byWang (1995).

3.2.1. Elastic Scattering

In solid state, elastic scattering is the most important interaction leadingto electron deflection. It is a result of Coulomb interaction of an elec-tron with energy E with the potential of a core with atomic number Z.In the Rutherford approximation the differential scattering cross section isgiven by

dσd�=

1

(4πε0)2 ·

e4Z2

16E2 ·1

sin4 ( θ2

) . (1)

Therein, θ denotes the scattering angle and e is the charge of an electron.This formula is helpful in the interpretation of TSEM imaging, albeit

not absolutely exactly. For example, shell electrons are not considered thatscreen the nucleus from the traversing electron, eliminating interactionoutside the neutral atom. They are accounted for in the Wentzel model byan additional exponentially decreasing term (Wentzel, 1926).

For a thorough understanding, quantum mechanical and relativisticeffects such as spin-orbit coupling must also be considered. This leadsto Mott cross sections for which no analytical expression can be given(Mott and Massey, 1933). Figure 6 shows the ratio of Mott and Rutherfordscattering cross sections for a few materials. As can be seen, Rutherfordscattering is a reasonable approximation for low atomic numbers and high


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180150

12090

6030

0 0.1

CZ = 6

Scattering angle (°)0.5

15

Energy (keV)

1050

0.5

1.0

1.5

r (θ

)

2.0

2.5

3.0

Scattering angle (°)

180150

12090

6030

0 0.1

SiZ = 14

0.51

5

Energy (keV)

1050

0.5

1.0

1.5 r (θ

)2.0

2.5

3.0

3.5


180150

12090

6030

0 0.1

AuZ = 79

0.51

5

Energy (keV)

1050

1.0

2.0

3.0

r (θ

)

4.0

5.0

6.0

7.0

8.0


180150

12090

6030

0 0.1

UZ = 92

0.51

5

Energy (keV)

1050

1.0

2.0

3.0

r (θ

)

4.0

5.0

6.0

7.0

8.0

9.0

FIGURE 6 Ratio r(θ) of differential Mott and Rutherford scattering cross sections as afunction of electron energy and scattering angle (Frase et al., 2009), calculated with theMonte Carlo simulation program MOCASIM (Reimer et al., 1996). Image reprinted withkind permission from IOP Publishing.

electron energies, whereas large discrepancies occur for heavy elementsand low energies.

3.2.2. Inelastic Scattering

Inelastic scattering occurs due to interactions between the incident elec-trons and the electrons of the specimen. Various interaction mechanismstake place, all of which involve energy transfer (Reimer, 1998). A greatportion of the absorbed energy is eventually converted to heat, which isthe main reason for beam-induced sample damage.

The angular distribution of inelastic scattering (Figure 7) is restrictedmostly to very small angles (forward scattering), which are smaller than theacceptance angle of typical BF detectors. Thus the number of electronsregistered by a BF detector is hardly influenced by inelastic scatteringprocesses. Consequently, variations of the signal detected in BF or DFmode are predominantly due to elastically scattered electrons.


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104

103

Ar

102

ElasticWentzel

10−4 10−3 10−2 10−1radΘ

∼ Θ−2

∼ Θ−2

ΔE = 0 eV

ΔE = 11.7 eV

10

1

aH2

1 dσdΩ

FIGURE 7 Angular dependence of differential scattering cross section for elastic andinelastic scattering of electrons with an energy of 25 keV on an argon atom (normalizedby the square of the Bohr radius aH) (Reimer, 2008). Image reprinted with kindpermission from Springer Science+Business Media.

3.2.3. Electron Diffraction

Bragg diffraction is evoked by regularly arranged lattice planes of crys-talline materials. If electrons are reflected by two planes, positive inter-ference occurs at certain angles that fulfill the Bragg condition. AlthoughBragg diffraction is the most important contribution, further diffractioneffects may manifest themselves. For example, Fresnel fringes, which area result of diffraction at the edge of an aperture, have been shown usingTSEM (Broers, 1972).

Treacy and Gibson (1993, 1994) theoretically analyzed diffractioneffects by applying the concept of a coherence volume: Interference mayoccur if a second atom is located inside the coherence volume aroundthe first one. Coherent intracolumn scattering vanishes above a certainmaximum scattering angle (Volkenandt et al., 2010)

θmax = 2 arcsin(

0.61 ·√λ/d

), (2)


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where λ denotes the electron wavelength and d the lattice spacing. Thisknowledge may be used in Z-contrast imaging to avoid influences ofdiffraction effects (see Section 3.3.2).

3.3. Contrast Mechanisms

3.3.1. Mass-Thickness Contrast

In Section 3.2, scattering on a single atom was discussed. In solid samples,the probability of scattering events increases with growing sample thick-ness. The number of electrons that pass the sample without interaction canbe described by a simple exponential function as follows (Reimer, 2008):

n = n0 exp(−t3

)(3)

with the number of incident electrons n0, and the thickness of the sample t.The mean free path 3 describes the average length of the path that anelectron travels between scattering events. It is inversely proportional tothe number of atoms per unit volume N, which can be expressed as N =NAρ/ma with Avogadro constant NA, density ρ, and atomic mass ma:

3 =1

Nσ=

ma

NAρσ. (4)

The total scattering cross section of one atom, which depends on theenergy of the primary electron [see, e.g., Eq. (1) and Figure 6], is denotedby σ .

In Figure 8, the mean free path 3 is shown for different materials andelectron energies. Usually, the sample is thicker than the mean free pathand thus multiple scattering occurs. Consequently, the electron beam isbroadened on its way through the sample. By varying the accelerationvoltage and thus the electron energy, the extent of beam broadening canbe changed to optimize the contrast of TSEM images.

For a constant electron energy, beam broadening grows with densityand thickness of the specimen (Figure 9). At the same time, more elec-trons are scattered to high angles and are registered by correspondingDF detectors. Consequently, the BF signal decreases. For example, theBF and DF images shown in Figure 4 reveal the divergent TSEM signalintensities for silica spheres (high mass thickness), carbon film (low massthickness), and holes in the film. BF and DF imaging is usually dominatedby mass-thickness contrast, although it may be complemented by furthercontrast mechanisms (e.g., by diffraction contrast in the case of crystallinesamples).


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15

10

5

00 5 10 15 20

Electron energy (keV)

C, Z = 6

AI, Z = 13

Cu, Z = 29

Ag, Z = 47

Au, Z = 79

Mea

n fr

ee p

ath

(nm

)

25 30 35

FIGURE 8 Mean free path for varying electron energy and different materials, datataken from Reimer (1998).

FIGURE 9 Illustration of beam broadening by a sample of varying thickness andelemental composition: Only a few electrons are scattered out of the central beam by athin sample of light material (left). The number of scattered electrons grows if thesample gets thicker (center) and if its density increases (right) (Goodhew et al., 2001).Image reprinted with kind permission from Taylor & Francis.


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Initially, DF signal intensity grows if mass thickness is increased, butthis simple relationship does not hold constantly. At a certain thickness,fewer electrons will be able to penetrate through the sample due toabsorption and backscattering, and thus the signal intensity decreases.Even contrast inversion is possible if the thickness is increased further(Grillon, 2006; Morikawa et al., 2006).

3.3.2. Z-Contrast

The deflection of electrons to large angles is dominated by elastic scat-tering, which depends strongly on the element as can be noted fromthe Rutherford formula [Eq. (1)], which reveals that the scattering crosssection is proportional to the square of the atomic number: dσ/d� ∼ Z2.This dependence is the origin of the term Z-contrast, which is also calledmaterial contrast.

Consequently, by detecting only those electrons that are scattered tohigh angles, information about the elemental composition of the speci-men may be obtained. This is done with the help of an HAADF detector.To avoid influences due to diffraction effects, its angular acceptance rangeshould be confined to angles larger than the maximum angle for coherentintracolumn scattering [Eq. (2)], which is discussed in Section 3.2.3 (Volke-nandt et al., 2010). For example, Figure 10 shows the minimum HAADFdetection angle for different primary beam energies.

Z-contrast is one of the most important imaging modes in STEM, but itwas only occasionally exploited in TSEM. Since TSEM detectors that offerextra detector segments for HAADF are now commercially available, theuse of Z-contrast imaging is growing (see Section 6 for examples).

0.35

0.30

0.25

0.20

0.15

0.10

0.050 25 50 75 100

Primary beam energy (keV)

Det

ecto

r in

ner

radi

us (

rad)

125 150 175 200

FIGURE 10 Minimum HAADF detection angle to avoid influences of diffraction effectswhile studying a GaAs sample (Volkenandt et al., 2010). Image reprinted with kindpermission from Cambridge Journals.


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3.3.3. Diffraction Contrast

In STEM, electron diffraction is visualized with the aid of a projectionlens underneath the sample, which enables the detection of diffractionpatterns. The examination of diffraction effects may also be conductedin TSEM, although projection lenses are usually not available. Two tech-niques have been described for this purpose.

In the so-called Grigson technique, scanning coils are placed under-neath the sample (Figure 11). Two imaging modes may be realized. Ifthe incoming beam is kept fixed at a certain sample position and theextra coils are used for scanning an angular range, the electron diffrac-tion pattern is recorded that originates from the sample area hit by theelectron beam. Alternatively, the incoming electron beam may be scannedacross the sample while the coils below the sample are adjusted to recorda fixed diffraction order. In this way, different crystalline structures canbe resolved laterally. Using the Grigson technique, information is avail-able concerning the type of lattice, its spacing, defects, and so on (Joy andMaher, 1976).

The rocking beam technique was used only occasionally because itsapplication is more elaborate than that of the Grigson technique. It is theelectron-optical reciprocal of the Grigson technique without the need forextra scanning coils (Van Essen et al., 1970). Instead of a variation of thedetection angle, the angle of incidence is varied. With an annular detectorusually used for DF imaging, the rocking beam technique adds the

Specimen

Scan coils

Detectoraperture

Diaphragm

Detector

FIGURE 11 In the Grigson technique, diffraction contrast may be depicted by scanningcoils positioned underneath the specimen (Reimer, 1998). Image reprinted with kindpermission from Springer Science+Business Media.


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detection of Kikuchi patterns to the possibilities of the Grigson technique(Woolf et al., 1972).

3.4. Monte Carlo Simulation of TSEM Signals

Accurate quantitative measurement results based on SEM or TSEMrequire an understanding of the image formation process (Frase et al.,2007; Postek and Vladar, 2011). An established method to gain insightinto SEM image generation is the simulation of electron sample inter-actions by means of Monte Carlo methods (Frase et al., 2009). In MonteCarlo simulations, stochastic physical events are modeled using randomnumbers. Whereas a single simulation is more or less meaningless, manysimulations of the same physical process lead to meaningful information.Since the first implementations of Monte Carlo simulations in the 1960sthey have grown powerful due to the exponential increase in comput-ing power, which allows the simulation of a large number of individualevents, thus reducing statistical noise to an adequate level. In addition,progressively more sophisticated and realistic algorithms can be used tonumerically simulate the imaging process involving secondary or trans-mitted electrons. The basis of these simulations is a physical model of theelectron scattering process in solid-state (Section 3.4.1) and appropriatemodels of electron detection (Section 3.4.2).

The interaction of primary electrons with the sample can be mod-eled on the basis of the fundamental scattering theory introduced above.The detection of the transmitted electrons is easily modeled, takinginto account the geometry and sensitivity of the transmission detector.Together this leads to robust and straightforward modeling of TSEM sig-nals, which is a significant advantage of TSEM imaging compared withthe detection of SEs: The generation, emission, and detection of SEs withenergies below 50 eV is considerably influenced by many minor factors,such as specimen charging, surface oxidation, carbon contamination, andelectromagnetic fields of SEM components (Frase et al., 2009). In practice,many Monte Carlo program packages use adjustment factors to achievesatisfactory agreement of simulated and measured SE signals (Frase et al.,2009). In contrast, these effects barely affect primary and transmitted elec-trons with energies of some ten keVs, thus making the simulation of TSEMimaging more reliable (Postek et al., 1993).

3.4.1. Electron Diffusion in Solid State

In Monte Carlo simulation of electron diffusion in solid state, individualelectron trajectories caused by scattering in the sample are determined.An electron that enters the sample at (x0, y0, z0) with an energy E0 travelsa distance s1 before it is scattered for the first time at (x1, y1, z1) (Figure 12).The scattering event leads to a change in the direction of travel specified


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x0, y0, z0

x1, y1, z1

x2, y2, z2

xn, yn, zn

s1

s2

ϕn

θn

FIGURE 12 Scheme of an electron trajectory iteratively calculated as the result of aseries of scattering events.

by the scattering angle θ1 and the azimuthal angle ϕ1. Subsequently, theelectron travels the distance s2 in this direction before it is scattered for thesecond time at (x2, y2, z2), leading to angles θ2 and ϕ2, and so forth.

The iterative simulation of scattering events stops when the electronleaves the sample. If its direction of travel has been reversed and itemerges from the sample surface in backward direction, it becomes a BSE.If thin samples are examined, most electrons cross the sample and emergefrom its bottom as transmitted electrons moving in the direction speci-fied by θ = θn and ϕ = ϕn. It is also possible for the electron to lose all itsenergy along its path, leading to absorption by the sample.

Starting with the distance s1, all parameters are determined randomly.The probability p(s) that an electron has not yet been scattered at distances depends on the mean free path 3 (Reimer, 1998)

p(s) = exp(−s3

). (5)

The probability that a scattering event occurs is given by

P(s) =

∫ s0 p(s)ds∫∞

0 p(s)ds= 1− exp

(−s3

), (6)

which can be simulated by a uniformly distributed random number R ∈(0, 1]. Depending on R, the traveled distance is determined by the inverse


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function

s(R) = −3 ln(1− R) (7)

or by s(R) = −3 ln(R), which is equivalent because both R and (1− R) areuniformly distributed between 0 and 1.

The scattering angle θ of elastic scattering events is usually determinedby differential Mott scattering cross sections (Mott and Massey, 1933).Because no analytical expression can be given and their numerical calcula-tion is elaborate and time-consuming, they must be calculated beforehand.Czyzewski et al. (1990) compiled a dataset used by Browning et al. (1994)to deduce an approximation formula. As an alternative, Salvat and Mayol(1993) published FORTRAN algorithms that enable everyone to computedatasets that are adapted to the simulation task at hand and to the desiredaccuracy. Values between precalculated data points are determined byinterpolation.

Because the azimuthal deflection of the electron is radially symmet-ric, the azimuthal angle ϕ is uniformly distributed and may be easilydetermined by

ϕ = 2πR (8)

with a second, independent, random number R as defined above.Inelastic scattering is characterized by small scattering angles (see

Section 3.2.2) and energy losses usually not exceeding 50 eV, which issmall compared with the initial energy of primary electrons. The smalldeflections may often be neglected and inelastic scattering events donot necessarily have to be simulated individually. Instead, a continuousenergy loss of the primary electrons may be assumed along their paththrough the sample. This so-called continuous slowing down approxima-tion (CSDA) was introduced by Bethe (1930). The mean energy loss dE perpath element ds is called stopping power S. For nonrelativistic energies thesimplified Bethe formula reads

S =∣∣∣∣dE

ds

∣∣∣∣ = NAe4

8πε20

ZAE

ln(

1.166EJ

)(9)

with the atomic weight A. The stopping power depends on the ionizationenergies of the shell electrons, which are generalized by a mean ionizationenergy J. Its value (in eV) can be approximated as (Berger and Seltzer,1964)

J = 11.5Z for Z ≤ 12

J = 9.76Z+ 58.5Z−0.19 for Z ≥ 13. (10)


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This approach fails if the electron energy is too small and the loga-rithmic term in Eq. (9) becomes negative, describing an increase in theelectron energy, which is physically impossible. Based on a semi-empiricalapproach and physical considerations, Joy and Luo (1989) introduced amean ionization energy J′ that depends on the energy of the electron andreplaces J in Eq. (9):

J′ =J

1+ kJE

. (11)

The variable k is close to but always less than unity. Because the stoppingpower is not strongly sensitive to k, the small differences between its val-ues for various elements may be neglected and a constant value of 0.8576may be chosen to simplify the argument of the logarithm in Eq. (9), whichthen reads (Joy, 1991)

S =NAe4

8πε20

ZAE

ln(

1.166EJ+ 1

). (12)

3.4.2. Detection of Transmitted Electrons

After a transmitted electron emerges from the sample at (x, y, z) withenergy E, its direction is specified by the angles θ and ϕ (Figure 12).The normalized signal intensity registered by a detector can be writtenin general form as

I =∑

i h(θi,ϕi, xi, yi, zi) · s(Ei)

N0 · s(E0). (13)

Therein, N0 is the number of primary electrons impinging on the sam-ple, s(E) denotes the energy sensitivity of the detector, and the accep-tance function h(θ ,ϕ, x, y, z) describes whether or not an electron hits thedetector (h = 1 or h = 0, respectively).

At high magnifications, the detector dimension is orders of magnitudelarger than the field of view; hence the exact emerging position (x, y, z) andeven the different scan positions of the electron beam may be neglected.Since transmission detectors are usually radially symmetric, the depen-dence on the azimuthal angle ϕ also vanishes. A transmitted electron isregistered if the angle θ lies between the minimum and maximum detec-tion angles of the respective detector segment: θmin ≤ θ ≤ θmax. This canbe expressed as

h(θ ,ϕ, x, y, z) ≈ h(θ) = H(θ − θmin) · H(θmax − θ) (14)


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14

0.5

0.4

Sen

sitiv

ity (

1/pA

)

0.3

0.2

0.1

016 18 20 22

Electron energy (kV)

24 26 28 30

FIGURE 13 Electrons with energies below a threshold of 13 kV are omitted by thesolid-state detector whose sensitivity increases linearly above this value (Buhr et al.,2009). Image reprinted with kind permission from IOP Publishing.

using the Heaviside function H. If the assumptions made above are notvalid—for example, due to azimuthal subdivided detectors or due to lowmagnifications—corresponding geometric considerations lead to otherexpressions for h(θ ,ϕ, x, y, z).

The sensitivity s(E) depends on the type of detector. In the simplestcase of sufficiently fast electron counting, it is unity. Another example ispresented in Figure 13, which shows the linear sensitivity of the detectorused for nanoparticle characterization in Section 5.3.

To summarize, two parameters that can be easily determined are usu-ally sufficient to characterize a transmission detector, its acceptance func-tion h(θ), and its sensitivity s(E). The acceptance function is determined onthe basis of the detector geometry, whereas the energy-dependent sensi-tivity can be measured by varying acceleration voltage and probe currentin the absence of a sample.

3.4.3. Two Simulation Programs: MONSEL and MCSEM

Various Monte Carlo program packages are available for the simulationof SEM image formation, which differ in the implementation of electronspecimen interaction and in their intended use (Frase et al., 2009). Thesepackages are usually developed to simulate SE and BSE emission, butsome may also be used to simulate transmitted electrons. Two examples


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are presented by Young et al. (2008) and by Demers et al. (2011). In thefollowing, two simulation programs—MONSEL and MCSEM—are intro-duced, which were used in the analysis of linewidths and nanoparticles,respectively, as described in Section 5.

The Monte Carlo simulation program MONSEL was implemented atthe National Institute of Standards and Technology (NIST) by Lowney andMarx (1994) on the basis of an older Monte Carlo code of Myklebust et al.(1976). A predecessor of MONSEL approximated Mott cross sections by ascreened Rutherford potential multiplied by the factor (1+ Z/300) (Posteket al., 1993), while later Browning’s formula (1994) is used. Inelasticscattering is modeled by the CSDA.

MONSEL was specifically designed for the application discussedin Section 5.2: the examination of X-ray masks using transmitted andbackscattered electrons. For example, the implementation of the home-built transmission detector was optimized using simulation results(Postek et al., 1993). In the initial version, MONSEL-I, the sample geom-etry was restricted to one line on top of a substrate consisting of up tothree layers, and the excitation of quasi-free valence electrons was treatedas the only reason for SE emission using Moller’s cross section (1932).

Subsequently, the code has evolved to be more flexible and to incorpo-rate new implications, such as the proximity effects of neighboring lines(Lowney, 1995b). With the second version, MONSEL-II (Lowney, 1995a),the treatment of SEs was improved using Kotera’s (1990) formalism forthe generation of plasmons and their decay into SEs. MONSEL-III intro-duced a new specimen structure, a 2× 2 array of finite lines, and allowedtwo-dimensional (2D) plots (Lowney, 1996). Recently, MONSEL has beenrewritten in Java and merged with NISTMonte (Ritchie, 2005) to simu-late arbitrary 3D specimens such as transistors (Postek and Vladar, 2011;Villarrubia et al., 2007).

The Monte Carlo Simulation for Electron Microscopy (MCSEM) wasdeveloped at the Physikalisch-Technische Bundesanstalt (PTB), Ger-many’s national metrology institute (Gnieser et al., 2008; Johnsen et al.,2010). It is a general-purpose simulation program that provides thoroughand realistic modeling for SEM and electron beam lithography up to accel-eration voltages of 50 kV. It has been implemented in C++ (Stroustrup,2003) using object-oriented techniques. In the meantime, a second versionhas been written in Matlab (Matlab, 2009). A key feature of MCSEM isits modular design leading to great versatility. The software may be eas-ily adapted to new simulation tasks by enhancing existing modules orby integrating new ones. Third-party code may also be readily adapted.Figure 14 shows the standard modules for input and output, which inter-act with the core module that simulates diffusion of primary electrons inthe specimen using precalculated Mott cross sections (Salvat and Mayol,


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Chargemodel

3D Specimenmodel

3D Electronbeam

Core:Electron diffusion

in solid state

Scattering:– Mott cross section– Bethe approximation

Detector model:SE / BSE / TE yieldElectron trajectoriesExit angles and energies

Output:Image processing

FIGURE 14 Overview of the program modules of MCSEM grouped into input (left),core (center), and output (right).

1993) as well as SE generation and emission based on a semi-empirical,parametric model (Joy, 1987; Seiler, 1983). The CSDA in the modificationof Joy and Luo (1989) is used to describe the energy loss of the electrons.

Aside from point sources and Gaussian beams with different diame-ters, parallel and conical illumination can also be modeled at any focalplane. Arbitrary specimen structures with free elemental compositionmay be simulated in two or three dimensions. The structures are com-posed of possibly overlapping, basic predefined geometric bodies that canconsist of different elements or compounds.

Whereas charging effects are supposed to have little effect on high-energy primary electrons transmitting through thin samples, they may becrucial for SE imaging. The transport of SEs in electromagnetic fields isimplemented into MCSEM, and specimen charging also can be modeledon the basis of dielectric properties of the material.

Different detectors may be modeled at the same time in MCSEM,based on the known exit angle and energy of all individual secondary,backscattered, and transmitted electrons. The simulated signals at adja-cent points may be composed as linescans producing signal profiles or aswhole synthetic grey-scale SEM or TSEM images. Furthermore, electrontrajectories in the specimen may be visualized in three dimensions.Figure 15 shows a number of electron trajectories in a nanosphere.

4. TSEM COMPARED WITH COMMON ELECTRONMICROSCOPY TECHNIQUES

4.1. Resolution

In this section, the main effects that limit the lateral resolution of TEM,STEM and SEM are discussed and compared with TSEM.


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FIGURE 15 Hundred individual electron trajectories of beam electrons transmittingthrough a latex sphere having a diameter of 100 nm.

4.1.1. Chromatic Aberration

While the energy spectrum of the escaping electrons is quite narrow forthin samples examined in a TEM, energy losses of the electrons due toinelastic scattering on their way through the specimen are more diversefor thicker samples. Due to the resulting broader energy spectrum, theimpact of chromatic aberration of the objective lens behind the samplegrows, which leads to a variation in focal length for electrons exhibitingdifferent energies. The loss of resolution may be seen in Figure 16a, whichshows indium crystals on a thin Formvar film covered with a latex sphere1.1 µm in diameter (see Figure 16d). In comparison, Figure 16b showsthe same sample imaged with STEM. Because no objective lens is used inSTEM (and TSEM), the achievable resolution is not limited by chromaticaberration and depends mainly on the size of the beam spot.

4.1.2. Beam Broadening

In STEM and TSEM, the resolution of thick samples is influenced by thetop-bottom effect (Gentsch et al., 1974): Because the beam size widens bymultiple scattering on the way through the sample, the resolution of fea-tures at the bottom of the sample is inferior to that at the top. As shownin Figure 16c, a sphere placed underneath the indium crystals leads toreduced intensities but the crystals can be depicted with high resolu-tion. In contrast, if the sphere is on top of the crystals it leads to beambroadening, which results in blurred edges and reduced resolution (seeFigure 16b).


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(a) (b)

100 kV, STEM, Top

(d)

100 kV, TEM, Bottom 100 kV, STEM, Bottom

(e)

Scanningelectron probe

Formvar film

EvaporatedIn layer

Polystyrenesphere

(c)

FIGURE 16 Demonstration of chromatic aberration (discussed in Section 4.1.1) and thetop-bottom effect (Section 4.1.2). The TEM image (a) of a layer of indium crystalsdeposited on a Formvar film and covered by a latex sphere (d) is blurred due tochromatic aberration of the objective lens. The STEM image of the same sample (b)shows mediocre image quality due to beam spreading. Sharp STEM images (c) may betaken if the sphere is located underneath the indium crystals (e). The arrows indicate thesame elongated feature. Results first published by Gentsch et al. (1974). Images reprintedwith kind permission from Springer Science+Business Media (Reimer, 2008).

Concerning resolution, TSEM is somewhat inferior to STEM due tothe reduced acceleration voltage and technical constraints. By going fromsome hundred kilovolts to below 30 kV, the impact of lens aberrationsincreases and thus the beam diameter grows. The extended focal length


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further deteriorates the size of the electron probe. Therefore, the distancebetween the final lens and the sample should be kept as small as prac-tical. Consequently, in a STEM, the sample is placed inside the electroncolumn for best resolution. A further advantage of this assembly is therigid interconnection between the sample and the electron-optical system.In contrast, sample stage and electron optics move more or less indepen-dently in the case of TSEM. Hence, the resulting vibrations sometimesultimately limit the achievable resolution in practice, whereas the electronoptics is in principle capable of higher resolutions.

In more common SEM imaging modes, such vibrations play only aminor role because usually the resolution is limited by the size of the inter-action volume. BSEs and SEs are generated inside the interaction volumeand may be registered by corresponding detectors if their energy is highenough to exit the sample. As discussed in Section 2.2, the emission zoneof SEs and BSEs is larger than the beam spot, thus leading to a deteri-oration of resolution. One possible way to improve the resolution in SEand BSE imaging is to restrict the size of the interaction volume by reduc-ing the acceleration voltage. However, this also leads to a larger beamspot because the influence of the aberrations of the electron–optical sys-tem grows. Compared with those common SEM imaging modes, TSEMexhibits a higher resolution because it is not limited by the interaction vol-ume (Golla et al., 1994). Thus, maximum acceleration voltage may be usedfor best resolution.

4.2. Contrast

The size of the interaction volume not only affects the resolution but alsogoverns the achievable contrast. It limits the size of objects that may bedepicted using SEs (Goldstein et al., 2003). If the object is smaller thanthe interaction volume, the electron beam transmits through it and entersthe substrate. The smaller the object, the more scattering and SE emis-sion take place in the substrate while the number of SEs emitted fromthe object decreases. This leads to a high background noise level, a poorSNR, and deteriorating contrast. At some point, scattering in the substratedominates and the contrast is no longer sufficient to observe the object ofinterest.

Emission of SE2 from the substrate is reduced if a thin film oflight material is used. In relation, more well-localized SE1 are detected.Although the noise is reduced, this does not help to raise the low overallsignal since the number of SEs emitted from the object due to inelastic scat-tering remains small. In comparison, many more elastic scattering eventsoccur, which mostly lead to deflections of the primary electrons sufficient


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to distinguish them from unscattered ones. Consequently, small objectscan still be imaged with good contrast in TSEM mode.

The contrast of TSEM images can be improved further by reducingthe acceleration voltage as discussed in Section 3.3.1. This is particularlyimportant for low-Z materials such as biological samples (Section 6.1),as well as for samples composed of materials exhibiting only small dif-ferences in density—like polymer blends (Section 6.2). Being limited to amaximum acceleration voltage of 30 kV, TSEM is able to show more detailsof such samples than dedicated high-voltage instruments. Most currentSEMs can be operated at acceleration voltages as low as 1 kV, or even less,with still quite good resolution (Pawley, 1992).

4.3. Energy-Dispersive X-Ray Spectroscopy

If the electron beam transmits small objects of interest, EDX spectra alsodeteriorate because X-rays are mainly generated in the substrate. BecauseX-rays are able to escape not only close to the surface but basically fromthe whole interaction volume, the resolution and contrast of EDX are usu-ally even worse than in SE imaging. Therefore, EDX analysis benefitsextraordinarily from thin supporting films and, thus, from reduced inter-action volumes common in TSEM (Kotula, 2009; Laskin and Cowin, 2001;Maggiore and Rubin, 1973; Vanderlinde and Chernoff, 2005), leading tohigher resolutions approaching the size of the electron beam for very thinsamples.

Since there is no more need to confine the interaction volume, largeacceleration voltages become possible, leading to additional high-energypeaks in the X-ray spectrum. These peaks generally facilitate the identifi-cation of elements and without them, the determination of some elementswould not be possible at all. However, minimizing the interaction volumealso leads to a decrease in count rate, which needs to be compensated bylonger integration times. Also, in conjunction with a transmission detec-tor, attention must be paid to peaks originating from the detector (Habichtet al., 2001).

4.4. Sample Preparation and Throughput

Due to the restriction to electron-transparent specimens, the preparationof bulk samples for TSEM is as sophisticated and time-consuming as in thecase of TEM. Fortunately, there is a rich knowledge of TEM sample prepa-ration techniques available that may also be exploited for TSEM (Ayacheet al., 2010). For example, TEM grids are also conveniently used.


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In the case of focused ion beam (FIB) milling, TSEM is a valu-able tool to speed up sample preparation (Kendrick et al., 2008).Instead of transferring the milled sample to a TEM, quality control andbasic analysis can be accomplished parallel to milling if the SEM isequipped with both FIB capabilities and a transmission detector (see alsoSection 6.3).

A serious constraint of dedicated high-voltage instruments is thereduced space that is available for the sample. Consequently, only onesample may be analyzed at a time and the size of the sample is confined.In contrast, arbitrary sample sizes and shapes can be examined by TSEMdue to the large specimen chamber of an SEM. Furthermore, the specimenchamber can accommodate many samples simultaneously, enabling theirbatch analysis without the need to break and reestablish the vacuum. Forexample, up to 12 samples can be mounted on the turret-type multisam-ple holder shown in Figure 17. They may be exchanged simply by rotatingthe sample stage.

This leads to a considerable speed-up of batch sample analysis that isalso facilitated by further advantages over high-voltage techniques thatTSEM shares with SEM. In addition to versatility and relative ease of use,there is the advantage of better and faster orientation due to the possibilityof very low magnifications. The acquisition and maintenance costs of anSEM with a transmission detector are significantly lower than those ofTEM or STEM. Thus, TSEM cuts down on time and cost per sample.

FIGURE 17 Multisample holder used in Zeiss SEMs for TSEM examination.


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PART II: APPLICATIONS

5. TRACEABLE DIMENSIONAL MEASUREMENTSOF NANOSTRUCTURES

Today’s modern life—for example, electronic devices incorporatingintegrated circuits—is based largely on progress in manufacturing struc-tures with dimensions at the nanometer level. Because the functionalityof these structures depends critically on the structure size, accurate andreliable measurement technologies for verification and quality assuranceare required. SEM is often used for this purpose since it offers lateralresolutions in the nanometer range.

A quantitative indication of the quality and reliability of measurementresults is the measurement uncertainty, which characterizes the dispersionof the measured results (Joint Committee for Guides in Metrology, 2008b).Typically, the measurement uncertainty is stated for a 95% coverage prob-ability, meaning that the true value lies in the stated interval with a proba-bility of 95%. The Guide to the Expression of Uncertainty in Measurement(GUM) establishes the general rules for evaluating and expressing uncer-tainties (Joint Committee for Guides in Metrology, 2008a). In general, themeasurement uncertainty consists of numerous contributions from themeasuring instrument, the sample under test, and from the measuringprocedure.

To enable comparability of measurement results—for instance, bet-ween different methods and instruments—the scale of the dimensionalmeasurement needs to be traceable to the definition of the SI unit meter.This is ensured by relating the measuring result to a reference through anunbroken chain of calibrations (each of which contributes to the measure-ment uncertainty). Thus for traceable measurements, a calibration of themeasuring instrument must be performed. One possibility is the use ofan adequate reference standard as described in Section 5.1. In the remain-der of this section, two applications of TSEM for traceable dimensionalmeasurements are discussed: linewidth measurements of masks used inX-ray lithography (Section 5.2) and, in more detail, size measurements ofnanoparticles (Section 5.3).

5.1. Calibration of an SEM

In SEM measurements, pixelated images are obtained and the relation tothe SI unit meter must be established by a calibration procedure. The pixelsize depends on the SEM parameters set during image acquisition, such asmagnification and scan speed. SEM manufacturers typically calibrate theirinstruments for a couple of parameter settings and interpolate between


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them. This is not sufficient for highly accurate measurements becausethe conversion factors determined in this way are typically exact onlywithin some percent, traceability is not ensured, and the uncertainty ofthe calibration is not stated by the manufacturer.

A simple approach to calibrate an SEM is the use of a calibratedgrating—that is, an artifact containing features with known dimensions.For example, in the following, the calibration of the SEM that was usedfor nanoparticle sizing (Section 5.3) is discussed. A grating from AdvancedSurface Microscopy was used (Chernoff et al., 2008), which consists of a 2Dpattern of aluminum bumps on silicon (Figure 18). The mean grating pitchof about 144 nm is calibrated with an uncertainty of less than 10 pm usingtraceable ultraviolet laser diffraction (Buhr et al., 2007).

The calibration of the SEM revealed two interesting points that needto be considered for highly accurate measurements using an SEM. First,the pixel sizes along the fast scanning direction (x-axis) and slow scan-ning direction (y-axis) differ by some tenths of a percent. This may beattributed to the repositioning of the beam in the y direction after lines-cans in the x direction. The so-called leading edge distortion is the secondeffect to be considered. The scan speed and thus also the pixel size of thefirst 200 pixels in the x direction differ from those of the remaining image

FIGURE 18 Micrograph of the 2D grating used for calibration, taken in SE mode.


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4.5

4.4

4.3

Pix

el s

ize

(nm

)

4.2

0 200 400 600

x-coordinate in pixel

800 10004.1

FIGURE 19 Due to the leading edge distortion, the pixel size of the first 200 pixelsdeviates from that of the remaining image.

(Figure 19). Therefore, these pixels are omitted during both calibration andmeasurement.

An alternative way to ensure traceability of SEM measurements isapplied for mask metrology (see Section 5.2). In this approach, a samplescanning technique that includes the measurement of the sample positionby interferometry is used; thus, there is no need for the calibration of pixelsizes using an artifact. On the other hand, this approach is feasible onlyfor capturing one-dimensional (1D) signal profiles. Making full 2D imageswould take too long because moving the stage is significantly slower thanscanning the electron beam.

5.2. Mask Metrology

The production of integrated circuits (IC’s) relies on a lithographic processto pattern functional layers on top of silicon wafers. Optical lithog-raphy is still able to fulfill the demands of today’s integrated circuitproduction due to constant technological improvements. Nevertheless,a number of alternative production techniques have been and are stilldiscussed to overcome the physical limits of optical lithography. Amongothers, the use of X-rays instead of light has been proposed (Cerrina,1992; Peckerar and Maldonado, 1993). This approach used a mask con-sisting of a thin X-ray–transparent silicon membrane with opaque goldstructures on top. The use of electron beams and so-called SCALPEL


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masks (for SCattering with Angular Limitation Projection Electron beamLithography) also has been proposed as another alternative to opticallithography (Harriott, 1997; Waskiewicz, 1997). These masks consist of a150-nm thin electron-transparent membrane of silicon nitride, structuredwith bilayers of chromium and tungsten.

Farrow et al. (1997) stated that for the analysis of SCALPEL masks,the TSEM approach is particularly suitable due to exploitation of thesame interaction process that is used during exposure of the wafer. Theyconcluded that with the aid of simulations the achievable measurementuncertainty was not limited by the instrumentation but by the measure-ment object due to disturbing line edge roughness of the SCALPEL testmask.

Because X-ray masks are also essentially electron transparent, TSEMwas also proposed as an ideal tool for traceable measurements of thelinewidth of the gold structures (Postek et al., 1989). In the followingdecade of research, Postek and coworkers accomplished fundamentalwork on traceable TSEM measurements as well as on its simulation.Since their work remains relevant for TSEM measurements to this day,it is outlined in this section regardless of the fact that X-ray lithographyhas ultimately not been implemented on a large scale due to difficultiesassociated with the production and handling of the fragile masks.

In 1989, Postek et al. laid out the basic concepts of X-ray mask metrol-ogy using TSEM and presented first experimental results, demonstratingnanometer precision (Postek et al., 1989). The authors stressed the advan-tages of using transmitted electrons instead of SEs. The width of linestructures can be determined with higher accuracy because no bloomingof the edge occurs. Furthermore, linewidth measurements using TSEMare sensitive to the base of the line, which is relevant to lithography. Fur-ther advantages of TSEM include the inspection of X-ray masks for defectssuch as particles and voids (Postek et al., 1991). Buried voids cannot beseen using SEs and the influence of low-Z particles may be exaggerated bySE imaging due to topographic contrast mechanisms. In contrast, TSEMimaging is a good approximation of the X-ray illumination of the wafer.Voids manifest themselves as contrast differences and the detection of par-ticles is restricted to those that block electrons and are thus prone to alsoblock X-rays.

In a comprehensive contribution to the subject, Postek et al. (1993) gavea detailed description of the instrumentation, the measuring approachbased on simulations, and the main factors influencing measuring accu-racy. They used a standard SEM retrofitted with a transmission detectorand an interferometer stage. Instead of scanning the electron beam, thespecimen stage was scanned while the electron probe was fixed. From thesignal profile (Figure 20a), the edge of the feature of interest, usually a


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(b)(a)

FIGURE 20 Measured (a) and simulated (b) transmission signal profiles across a goldline with a linewidth of 500 nm showed good agreement (Lowney, 1995b). Note the smallnotch on the sidewall. Increasing transmission is plotted downward to resemble theactual shape of the line. Images reprinted by permission of John Wiley & Sons, Inc.

gold absorber line, could be deduced by comparison with simulations.The findings of the simulation were also used for an improved detec-tor design: Because the silicon membrane resulted in a small amount ofunscattered electrons, a semiconductor transmission detector with a largeacceptance angle was used to maximize the detected TSEM signal. Thedetector was insensitive for electrons with energies below 3–5 keV; thusSEs were omitted.

Uncertainty contributions of the instrument as well as those fromthe X-ray mask were analyzed, revealing some advantages of the TSEMmethod compared with SE imaging. One is its robustness to misalignment.Due to the large detector area, axial alignment and spacing between detec-tor and sample were not critical. Also, carbon contamination was noproblem under typical measurement conditions because it did not notice-ably affect the high-energy primary electrons. However, the edges of thestudied gold absorber lines were slanted by a few degrees and TSEM wassensitive to the side wall angle. Changing the angle resulted in significantchanges of the signal profile. Thus it was important that the surface ofthe mask was perpendicular to the electron beam and that the slope anglewas accurately determined. Unfortunately, line edge roughness randomlyaffected the effective slope angle. The determination of the linewidthsrelied on Monte Carlo simulations using a predecessor of MONSEL (seeSection 3.4.3) that used the slope angle as an input parameter; thus theachievable accuracy was limited by those mask imperfections. For exam-ple, the combined uncertainty for the measurement of a 250-nm line wasestimated to be 10 nm.

The simulations revealed the presence of a small notch in the linescan(Figure 20b). However, in practice, the SNR was often not sufficient to


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reproducibly reveal the notch in the experimental data. After improvingthe instrument by using a field emission gun and reducing the distanceof the sample, the notch could finally be routinely resolved as shown inFigure 20a (Lowney, 1995b). Subsequently, the notch served as a beneficialpoint to determine the linewidth with reduced uncertainties by automaticalgorithms.

5.3. Nanoparticle Size Measurement

Nanoparticles, whose size is confined to the nanometer range in allthree dimensions, exhibit new properties different from the bulk material(Daniel and Astruc, 2004). Therefore, nanoparticles are increasinglyexploited not only in science and technology but also for improved con-sumer goods. Prominent examples of the latter are titanium dioxideparticles in sunscreen and silver particles in clothing.

Since the desired functionality of nanoparticles depends on their size,reliable size measurements are required. They may be accomplished byensemble techniques, such as dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS), that probe a large amount of particles atthe same time and provide their mean diameter. But ensemble techniquesshare disadvantages in case of complex size distributions or when mor-phological examinations are needed (Gleber et al., 2010; Rasteiro et al.,2008). Consequently, as a direct imaging method, electron microscopy isoften used to study nanoparticles. However, it struggles with poor statis-tics since probing many particles is usually time-consuming, especiallyusing TEM. SEM measurements in SE mode suffer from a blooming ofthe SE signal at the particle boundary, thus hampering reliable size mea-surements. In contrast, TSEM is perfectly suited for this measurement taskdue to high resolution (see Section 4.1) combined with simple and well-understood signal generation that is reliably simulated on the foundationof fundamental scattering theory (see Section 3.4).

TSEM has been used in a number of studies examining nanoparti-cles. Habicht et al. (2001, 2004, 2006) studied microtubules of differentshapes as well as metal nanoparticles whose synthesis uses the tubulesas templates. Probst et al. (2007) examined tin-palladium particles as smallas 5 nm incorporated into carbon nanotubes. They compared the resolu-tion for varying instrument parameters and detection modes. Maximumresolution was demonstrated by Tuysuz et al. (2008), who analyzed meso-porous particles by means of SE and transmission imaging. Barkay et al.(2009) were able to estimate the 3D shape of nanoparticles by combin-ing the 2D projection image with thickness information gained from thetransmitted intensities. Krzyzanek and Reichelt (2009) determined theheight of latex nanospheres by comparing annular DF signals with Monte


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Carlo simulations. Laskin et al. (2006) showed how to overcome the prob-lem of poor statistics by analyzing large numbers of particles, thanksto automation. They used computer-controlled SEM to study differentaerosol particles. The micrographs were composed of a mixture of BSEand DF TSEM signals, allowing the automated detection of a wide sizerange of nanoparticles made from various materials.

Whereas the contributions discussed above present a profound basis,none of the instruments used was traceably calibrated and no attemptwas made for highly accurate measurements. Aiming for traceablesize measurements of nanoparticles with small uncertainties, we devel-oped a dedicated TSEM measuring procedure (Buhr et al., 2009; Kleinet al., 2011). We calibrated the instrument as described in Section 5.1and used Monte Carlo simulations (Section 3.4) for quantitative anal-ysis of the experimental TSEM signals. In the remaining parts of thissection (Sections 5.3.1 to 5.3.4) our approach is presented, which hasbeen developed within the framework of a European joint research project(Implementing Metrology in the European Research Area, 2008). Exem-plary measurement results of gold particles are shown compared withTEM measurements.

5.3.1. Sample Preparation and Image Acquisition

Nanoparticles are often distributed in suspension. Sample preparationshould ensure that a representative fraction of the particles is present onthe substrate and it should facilitate their analysis. Therefore, a homo-geneous distribution of individual particles across a TEM grid withoutdrying artifacts is desirable. To achieve this, a droplet of the suspen-sion is deposited on TEM grids and after some time excess suspensionis removed using clean room tissue (Klein et al., 2011).

For the measurement, a Zeiss Leo Supra 35 VP is used, which isequipped with the solid-state transmission detector from K.E. Develop-ment (see Section 3.1.2). Micrographs are taken in BF mode with anacceptance half-angle of about 16 mrad, using an accelerating voltage of30 kV. Figure 21 shows some examples. Thanks to automatic acquisition,a series of TSEM images can be taken at a speed of more than one imageper minute in a predefined area of interest on the sample. Thus, the short-coming of poor statistics often associated with electron microscopy can beovercome.

5.3.2. Analysis of TSEM Images of Nanoparticles

In order to take advantage of the automatic image acquisition and to avoidthe risk of systematic deviations between different operators, an automaticimage analysis routine has been developed. The size is deduced from the


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(b)

100 nm100 nm

200 nm

(a)

(c)

FIGURE 21 TSEM images of three gold nanoparticle samples with nominal diameters of10 nm (a), 30 nm (b), and 60 nm (c).

projected area, which is determined by simply counting the pixels thatbelong to the particle. The critical task is to distinguish these pixels fromthe ones belonging to the background or to other particles. This is oftenaccomplished using global thresholding techniques (Sezgin and Sankur,2004). These techniques calculate a threshold based on the distribution ofgrey-scale values of the image, regardless of the depicted objects, whichleads to varying results depending on the chosen algorithm (Sadowskiet al., 2007).

For accurate measurements, a threshold determination based on thephysical effects of the image formation process is necessary. In ourapproach, the threshold has been determined by Monte Carlo simula-tions using the program package MCSEM (see Section 3.4). Assuminghomogeneous spheres, the output of the simulation is a signal profile ofa scan across the center of the particle. For example, Figure 22 shows thesimulated signal profile across a latex sphere.

The threshold signal Sthres at the boundary of the sphere can be easilydeduced and converted to the corresponding grey-scale value in a realTSEM image as follows:

gthres =Sthres − S0

S1 − S0· (g1 − g0)+ g0. (15)


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S1

0.7

0.5

S0

−90 −60 −30 0 30

x-coordinate (nm)

Particleboundary

60 90

g0

100

150

Mea

sure

d g

rey

valu

e

Sim

ula

ted

sign

al

200

Simulation

Experimentg1

gthresSthres

FIGURE 22 The simulated signal profile across a latex sphere agrees well with themeasured data (Klein et al., 2011). Image reprinted with kind permission from IOPPublishing.

The variables S0 and S1 denote the signal level in the center of the particleand in the background outside it, respectively (see Figure 22). They canbe related to the grey-scale values g0 and g1. The mean grey-scale valueof a few pixels at the particle’s center of mass yields g0, whereas g1 iscalculated as the median grey-scale value of the background pixels of theregion of intrest (ROI) around the particle. This approach is insensitiveregarding changes in brightness and contrast as long as the image is notunder- or oversaturated.

As shown in Figure 23, the threshold signal at the particle bound-ary depends on both the material and the particle diameter. This is dueto different scattering properties of various materials and to increasinginteraction paths for growing particle diameters, respectively (Klein et al.,2011). The interdependence can be taken into account by an iterative pro-cedure of the image analysis routine that determines threshold and sizeindividually for every single particle. Global thresholding (Prewitt andMendelsohn, 1965) is used to obtain an initial guess of the particle size.With this guess, an improved estimation of the threshold can be deter-mined, leading to an improved estimation of the size, and so on. After acouple of iterations both threshold and size remain stable.


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0.9Latex

Silica

Gold0.8

0.7

0.6

0.5

0.40 30 60 90

Diameter (nm)

Thr

esho

ld s

igna

l lev

el

120 150

FIGURE 23 The threshold signal at the particle boundary depends on both its size andmaterial (Klein et al., 2011). Image reprinted with kind permission from IOP Publishing.

The final threshold deduced from the iteration is used to separate theparticle from the background. Beforehand the ROI containing the parti-cle is interpolated to obtain subpixel accuracy. The particle size is thendetermined as the diameter of a sphere exhibiting the same projected area.

During image analysis, real particles must be distinguished from spu-rious objects, such as agglomerates or drying artifacts. For this purpose,three intuitive geometric parameters have to be set: minimum and maxi-mum particle size and minimum circularity. To simplify the selection, thesoftware presents all objects sorted by their size or by their circularity.

5.3.3. Uncertainty Budget

The uncertainty of the determined mean particle size consists of numer-ous contributions (Table 1), which are listed in this section in the orderof their importance. The largest part of the uncertainty budget is relatedto image analysis. The choice of minimum and maximum particle sizeand minimum circularity is often ambiguous with an interval of poten-tially appropriate values. The uncertainty is estimated from the impact ofvarying the parameters within the limits of these intervals.

The second most important contribution to the overall uncertaintyoriginates from the Monte Carlo simulations on which the determination


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TABLE 1 Overview over uncertainty contributions

Effect Contribution

Image analysis SignificantSimulation SignificantDigitalization MinorStatistics MinorDetermination of grey-scale values MinorCalibration of pixel size MinorPixel noise NegligibleSample preparation Unknown

of the threshold level used for image analysis relies. Beside general issuesrelated to Monte Carlo simulations, the main reason for this uncertaintyis the lack of knowledge about the diameter of the electron beam. Rea-sonable estimates range from 3 nm to 8 nm, and again the uncertainty isestimated from this interval, whereas a value of 5 nm is assumed for imageevaluation.

Digitalization errors occur because round objects are projected ontosquare pixels. Interpolating the image before size determination reducesthese effects.

Because the electron microscopic examination of many similar objectsis a tedious and time-consuming task, usually only a small number ofobjects are analyzed and thus electron microscopy is often associated withpoor statistics. On the contrary, the uncertainty attributed to statisticsplays only a minor role if reference samples with narrow size distributionsare measured and if thousands of particles are evaluated in reasonabletimes thanks to automatic image acquisition and analysis.

As stated in Section 5.3.2, the grey-scale values g1 and g0 in the back-ground and in the particle center, respectively, must be determined inorder to convert the simulated signal at the particle boundary to a grey-scale value required for thresholding the image. The determination maybe influenced by an inhomogeneous carbon background, drying residue,and so on, resulting in a corresponding uncertainty.

Based on the calibration described in Section 5.1, the pixel size can bedetermined quite accurately, thus contributing only to a minor extent tothe overall uncertainty. Pixel noise may lead to erroneous inclusion orexclusion of noisy pixels at the particle boundary, and thus to faulty par-ticle sizes. Compared with the other uncertainty sources, its effect can beneglected.


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If the mean particle size of a particle ensemble present in a suspensionis to be determined, sample preparation should ensure that a repre-sentative subsample is present on the substrate. While there are noobvious objections to the preparation technique used, its effect on thesize distribution, if any, has not yet been quantified. Thus the stateduncertainties do not include any preparation effects.

To determine the overall uncertainty, all stated uncertainty contribu-tions must be summarized quadratically, resulting in expanded uncertain-ties (coverage factor of k = 2, 95% probability coverage) associated withthe mean particle size of about one to a few nanometers. The stated uncer-tainty should not be confused with the statistical uncertainty component,which is sometimes mentioned exclusively. The statistical component isonly one part of the overall uncertainty and, in our case, it is a rathersmall one.

5.3.4. Measurement of Gold Particles and Comparisonwith TEM Results

Three gold nanoparticle standards have been chosen as test samples,which have been extensively studied by TEM (Kaiser and Watters, 2007)at NIST. These samples—named RM8011, RM8012, and RM8013—havenominal diameters of 10 nm, 30 nm, and 60 nm, respectively, and allowa comparison of our TSEM measurement results with results gained byTEM.

Table 2 presents a summary of the TSEM results. The expandeduncertainty of the mean diameter is as low as 1.2 nm for RM8011 andRM8012. For the 60-nm particles (RM8013) the uncertainty almost doublesto 2.3 nm. This is mainly due to a higher uncertainty contribution fromthe image analysis that could possibly be reduced if more particles weremeasured.

For comparison, the results of traceable TEM measurements are alsogiven in Table 2. The mean diameters determined by TEM are slightlysmaller than those measured by TSEM, but they are quite close and theyperfectly agree within the scope of the stated uncertainties. The differencesare only 0.2 nm for the 10-nm gold particles, 0.3 nm for the 30-nm parti-cles and 1.2 nm for the 60-nm gold particles. The report of investigation(Kaiser and Watters, 2007) states that “for TEM [...] reference values werecalculated from the ampoule means and the uncertainty level is based ona prediction interval approach (Neter et al., 1996), where the combineduncertainty is calculated as the standard deviation of the ampoule meansmultiplied by

√1+ 1/N (N is the number of ampoules analyzed) [. . .].”

Hence, the uncertainty values stated for the TEM results contain only


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TABLE 2 Results of TSEM measurements of three different gold particle samples andcomparison with NIST TEM data

TSEM TEM

Name RM8011 RM8012 RM8013 RM8011 RM8012 RM8013

Nominaldiameter (nm)

10 30 60 10 30 60

Mean particlesize (nm)

9.1 27.9 57.2 8.9 27.6 56.0

Expandeduncertainty of themean size (nm)

1.2 1.2 2.3 0.1* 2.1* 0.5*

Statistical standarddeviation of meansize (nm)

0.02 0.08 0.23 0.02 0.07 0.09

Spread of sizedistribution in nm(standarddeviation)

0.8 2.2 4.2 1.1 4.3 5.0

Median size (nm) 9.1 27.7 56.6 8.8 26.9 55.4Mode size (nm ) 9.4 28.1 55.8 8.8 26.7 55.8Number of analyzed

particles2318 747 325 5098 4364 3030

∗Solely based on statistics.

the statistical contribution, whereas the uncertainty analysis for TSEMmeasurements also includes important systematic influences as discussedin Section 5.3.3. Yet in the case of RM8012, the uncertainty associated withTSEM is smaller than the one ascribed to TEM measurements.

The size distributions of the samples are quite similar, resembling aGaussian distribution with the addition of a small fraction of larger parti-cles (Figure 24). Small but consistent differences can be seen by comparingthe size distributions determined by TSEM and TEM. Both size distribu-tions have similar widths but differ in their absolute position on the sizeaxis. The shift ranges from 0.3 nm to 1.3 nm, with TSEM yielding slightlylarger size values than TEM.

Whereas the different portions of large particles measured by TSEMand TEM may be an effect of subsampling or statistics, the small shiftbetween the size distributions measured by TEM and TSEM seems tobe systematic. Which distribution is closer to reality cannot be decidedbased on present knowledge. However, the differences are small com-pared with the stated uncertainties and the results of both measurementsfit well within the scope of those uncertainties.


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Rel

ativ

e fr

eque

ncy

0.3

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RM8012 TEMTSEM

Particle size (nm)

(b)

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02 4 6 8

RM8011 TEMTSEM

10

Particle size (nm)

(a)

12 14 16

FIGURE 24 Size distributions of three gold nanoparticle samples with nominal sizes of10 nm (a), 30 nm (b), and 60 nm (c) as measured with TEM and TSEM.


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6. CHARACTERIZATION OF DIFFERENT MATERIAL CLASSES

6.1. Biological Samples

From the very beginning, transmission detectors in SEMs were used toexamine biological specimens. Already in the first publication regardingTSEM, Kimoto and Hashimoto (1968) showed a micrograph of a cotyledoncell of a soybean seed to demonstrate the possibilities of the TSEM tech-nique. Nemanic and Everhart (1973) used a conversion stub for the studyof biological samples demonstrating the resolution of an 8-nm membranein a mitochondrion. Swift et al. (1969) modified a Cambridge Stereoscanfor transmission electron detection and examined the internal structureof keratin fibers (Swift, 1972a,b). Sample preparation was similar to TEMpreparation—namely, embedding in polymer, sectioning, and staining.Oho et al. (1987a) used a commercial SEM capable of transmission imag-ing and steadily improved it (e.g., using an adjustable detector aperture).Studying biological samples, a detailed comparison between TEM imagesand micrographs taken with the improved TSEM instrument revealedsimilar image qualities.

One of the main benefits of TSEM for biological applications is thegood contrast of light elements without the need for metal staining(Takaoka and Hasegawa, 2006; Takaoka et al., 2004) due to larger scat-tering cross sections for light elements at low electron energy. However,the use of low-energy electrons is a trade-off between higher resolution athigh energies but lower contrasts and higher contrasts at low energies butreduced resolution. Takaoka and Hasegawa (2006) also discuss the influ-ence of the support film in the imaging of biological samples. The granularimage of a carbon film overlaps the original image, thus degrading con-trasts. Therefore, support-free imaging using, for example, microgrids isdesirable for background-free images with high contrast.

The relatively large specimen chamber compared with TEM allowsthe integration of other instruments. Stemmer et al. (1991) fitted a scan-ning tunneling microscope (STM) in the specimen chamber of an SEM.The STM was tilted at 45◦ to allow for simultaneous TSEM and STMexamination of biological structures on electron-transparent films.

One step along the route to 3D reconstructions of the mammalian brainhas been taken using TSEM (Liu and Yorston, 2010). Such reconstruc-tions rely on a large number of ultramicrotome cross sections that must beimaged at high resolution. Using TEM to obtain these micrographs takesa tremendous amount of time. With the development of a new, powerfulsoftware the task of acquiring, storing, and combining many images canbe automated. After setting up a measuring task, the instrument can workunattended for days, generating combined images of whole tissues.

Hondow et al. (2011) established TSEM as “a practical tool fornanotoxicology.” They studied the in vitro uptake of nanoparticles andcarbon nanotubes in cells. For reliable results it is necessary to distinguish


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nanomaterial inside the cell from material that is solely deposited on topof the cell and also from preparation artifacts originating from micro-tome slicing. The combination of SE imaging using an in-lens detectorand transmission imaging proved to be an efficient method for thisdifferentiation.

6.2. Polymers

Besides biological specimens, TSEM is especially suited for the charac-terization of low-Z polymers due to increasing scattering cross sectionsfor low-energy electrons. This behavior enables high-contrast imagingand enhances the capacity to differentiate between materials of similaratomic composition. Consequently, the technique is successfully appliedto the study of polymers and rubber blends without the need for chemicalstaining (Cudby, 1998; Guise et al., 2011). Lednicky et al. (2000) used elec-tron energies of 5 keV to distinguish between individual components ofpolymer blends differing in density by as little as 0.04 g/cm3. Due tothe low-energy electrons, the preparation of sufficiently thin specimenscaused some difficulties.

TSEM is also beneficial for analytical X-ray mapping of polymer sam-ples. Beam damage and charging effects are avoided as far as possible(Brown and Westwood, 2003). Williams et al. (2005) used TSEM in an envi-ronmental SEM to provide both compositional and structural details ofthin films of semiconducting polymeric materials used in electronic appli-cations. Environmental TSEM imaging in the liquid state has been appliedto observe surfactant layers absorbed on the surface of latex particles(Faucheu et al., 2009).

In contrast to TEM, the large range of magnifications offered by TSEMenables the study of both large-scale phenomena such as crack propa-gation using low magnifications and nanoscale morphology of polymercomposites using high magnifications (Guise et al., 2011). Together withits flexibility in sample handling and the use of sample carousel systems,TSEM is a practical and affordable alternative to the TEM analysis ofpolymers.

6.3. Semiconductor

The small feature sizes in semiconductor devices require high-resolutionimaging for the inspection of the manufactured structures. A commonprocedure uses FIB preparation of thin cross sections in a FIB/SEM fol-lowed by TEM imaging (Giannuzzi and Stevie, 1999). This approachyields high-resolution cross-sectional images, but the effort to transferthe sample to the TEM is time-consuming. Often the ultimate resolutionoffered by TEM is not necessary, and in those cases, TSEM is a promisingand valuable alternative.


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In this field of application, TSEM can use its capabilities to full extentwhile being easier to use compared with TEM, resulting in an increasedthroughput and a reduced cost per sample. TSEM offers an imagingresolution that comes closer to TEM than ordinary SEM (Moore, 2003;Vanderlinde, 2002). Instrument parameters, such as acceleration voltageand detector acceptance angle, may be optimized to achieve high materialcontrast. For example, Young et al. (2008) showed that the contrast at theinterface between two materials can be enhanced if the HAADF detectoris composed of several segments allowing differentiation between variousazimuthal angular ranges.

TSEM can easily be incorporated into an FIB/SEM, allowing in situinvestigations and making tedious sample transfer redundant. With thehelp of a flipstage (Young et al., 2004) or of a special probe tip holder(Kendrick et al., 2008), the orientation of the sample can be adapted foreither FIB milling or TSEM observation. Due to its pronounced thicknessdependence, TSEM can be used to monitor and control FIB processingduring sample preparation (Golla-Schindler, 2008). Furthermore, TSEMenables direct thickness measurements based on appropriate model sim-ulations and can be used to ensure thickness uniformity (Young et al.,2008).

TSEM has been successfully applied to imaging, inspection, and fail-ure analysis of ICs (Coyne, 2002; Gignac and Wells, 2011; Nakagawaet al., 2002; Tracy, 2002); Figure 25 shows an example. Coyne et al. (2005)observed structural changes, such as defects in the crystal lattice andthermal-mechanical damage, which are induced by the micromachiningof wafer-grade silicon. Furthermore, high-resolution elemental analysis ispossible if TSEM is combined with EDX spectroscopy (Iannello and Tsung,2005).

(a) (b)

FIGURE 25 (a) The electron-transparent sample is ready for lift-off after FIBpreparation. (b) The BF TSEM micrograph of a faulty semiconductor structure clearlyreveals the defect as a dark spot (Gnauck, 2005). Images reprinted by permission of JohnWiley & Sons, Inc.


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6.4. Material Science

In addition to monitoring FIB thinning, the previously mentioned thick-ness and material dependence of TSEM may also be used for spatiallyresolved sample thickness determination or material concentration mea-surements. Since TSEM signals are—even for thin samples of a fewnanometers—the result of multiple electron scattering, quantitative mea-surements require appropriate model calculations or calibration proce-dures using samples with known properties.

Extensive work on this subject has been performed by Merli andco-workers in the first decade of this century. They used the TSEM tech-nique to measure arsenic dopant profiles in silicon (Merli et al., 2002),thereby demonstrating the verification of two monolayers of AlAs ina GaAs matrix (Figure 26). They also analyzed TSEM contrast and lat-eral resolution in dependence on sample thickness (Morandi and Merli,2007) and generalized their method for the study of biological samples(Morandi et al., 2007).

Pfaff et al. (2010) used HAADF detection at electron energies below30 keV to study electron scattering in amorphous carbon and carbon-based materials. Their experimental and theoretical findings quantita-tively revealed the relationships between TSEM signal intensity, samplethickness, and system parameters, such as electron energy and detectoracceptance angle. In addition to thin film samples, they also used wedge-type specimens to vary sample thickness in a wide range up to a fewhundred nanometers. The authors conclude that the method is capableof determining specimen thickness with a precision of 10%, provided that

250

200

Pix

el v

alue

150

0 200

35

810

20

40 nm 100 nm

40 nm 20 8 32510AlAs

1

t

GaAsGaAs GaAs

2 1 2 ml

2 ml1ml

600400nm

800 1000

FIGURE 26 The specimen shown as an inset was analyzed using a conversion-typetransmission detector. It consists of thin layers of AlAs (40 nm to 1 monolayer)sandwiched between slices of 100-nm GaAs. Layers as thin as two monolayers can beverified by averaging over 200 scan lines (Merli et al., 2002). Image reprinted withpermission from American Institute of Physics. Copyright 2002.


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the composition of the specimens is known. In another study (Volkenandtet al., 2010), HAADF-TSEM was applied to quantify sample thickness andindium concentration in InGaAs quantum wells. Here, the authors statethat thickness measurements can be performed with an accuracy of 5 nmand that composition changes of 5% can be detected.

Acevedo-Reyes et al. (2008) studied the application of TSEM to charac-terize precipitates in microalloyed steels, concluding that TSEM is a usefultechnique because it allows the analysis of large populations of precipitateparticles due to the high contrast of the micrographs. The application ofTSEM is restricted to cases where knowledge of the particles’ chemistryis not required. The authors prefer HAADF-TEM over TSEM to resolvethe chemical composition of very small precipitates with a size of a fewnanometers because these precipitates are difficult to analyze by EDX.

TSEM has been used to investigate the morphology of thin films of alu-minum and its alloys (Shimizu et al., 2004). The authors report that TSEMenables the study of fine film features and near-surface metal regionswith resolutions similar to TEM. In contrast to SE imaging, coating ofnon-conducting material is not required to avoid charging problems.

TSEM has also been demonstrated successfully in mineralogical appli-cations as a quick and easy method for imaging submicrometer-sized crys-tals in rock samples or for characterizing fine-scale intergrowths (Lee andSmith, 2006; Smith et al., 2006). Russias et al. (2008) used TSEM to studycalcium silicate hydrates, which are the main components of cement. Theycompared TSEM with high-energy TEM for this application, reportingthat TSEM causes less beam damage and may be regarded as a low-dosetechnique. However, TSEM could not provide information on the hydratecrystal structure; for this purpose, TEM is the appropriate means.

7. SPECIAL IMAGING MODES

7.1. TSEM in Liquids

Transmission detectors may also be used in so-called environmental SEMs(ESEM), which enable the investigation of samples in their liquid environ-ment. This combination has been called wet STEM by Bogner et al. (2005).The liquid layer must be thin enough to enable the transmission of elec-trons, which can be achieved by controlling the environmental conditionsin the ESEM chamber. The option of detecting the transmitted electronsis a significant extension of the ESEM technique since the entire liquidvolume can be imaged—that is, objects underneath the surface of theliquid are accessible and can be studied. This technique was used to inves-tigate suspensions in their wet environment (Bogner et al., 2007, 2005). It isalso ideally suited to the study of dynamic processes at the nanoscale, suchas colloidal crystal formation (Moh et al., 2010) or condensation processes


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(Barkay, 2010). Stokes and Baken (2007) imaged vacuum-sensitive softmatter nanomaterials in their native state.

7.2. Electron Energy-Loss Spectroscopy

Electron energy-loss spectroscopy (EELS) is a standard method for ele-mental analysis in TEM or STEM, featuring a very good spectral resolutionof less than 1 eV. In SEM, however, the standard method for elemen-tal analysis is EDX spectroscopy. Its energy resolution of about 100 eVis clearly inferior. Therefore, an EELS attachment for standard SEM intransmission mode is a promising alternative to EDX. Luo, Kursheed, andcoworkers developed a miniaturized EELS attachment that fits into a stan-dard SEM and works in transmission as well as in BSE mode (Khursheedet al., 2003; Luo and Khursheed, 2006, 2008). The instrument is second-order corrected for spherical aberration and has a spectral resolution ofabout 4 eV. As a demonstration, the K-edge spectrum of an amorphouscarbon film was recorded.

7.3. Tomography

Electron tomography is applied in both life science (Koning and Koster,2009) and material science (Kubel et al., 2005) to obtain high-resolution 3Dobject information. Typically, TEMs are used for electron tomography dueto the ultimate resolution achievable, but also because the damage to bio-logical objects is less severe at such high energies compared with electronenergies in the range of some ten kV. In material science, where crystallineobjects are often studied, STEM using HAADF detection is applied toavoid artifacts due to electron diffraction effects and to exploit the strongZ-contrast mechanism of HAADF detection.

High-voltage STEM images of low-Z materials have a weak object con-trast. The application of low-energy electrons as used in SEM improvesthe imaging of low-Z materials and thus may supersede the applicationof staining procedures. Recently electron tomography using transmis-sion detection in a SEM has been successfully applied to study proteins(Furusho et al., 2009). Sample damage effects due to low-voltage electronbeam irradiation could be effectively reduced by cooling the sample downto about 100 K.

Jornsanoh et al. (2011) introduced a new sample holder mounted to aneucentric tilting stage for tomography in an ESEM. They highlight the use-fulness of a tomography technique which is intermediate between X-rayand STEM tomography with regard to both resolution and sample size.They demonstrated the applicability of their device to study samples thatare difficult to image in STEM: filler particles incorporated into a polymer(Figure 27).


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1 µm

1 µm1 µm

FIGURE 27 Three-dimensional view of the filler particles inside a polymer sampleobtained by TSEM tomography (Jornsanoh et al., 2011). Image reprinted with kindpermission from Elsevier.

7.4. Visualization of Electric Fields

Sharp tips and protrusions such as carbon nanotubes or sharp emittertips can generate strong local electric fields. TSEM can be used for anin situ visualization of these fields. Fujita et al. (2007a,b, 2008) placedtungsten tips in a TSEM above the BF detector. They observed dark shad-ows around the apex of the biased tip because the electric field causeda deflection of the primary electron beam, resulting in a loss of BF sig-nal. The size and form of the shadow were recorded as a function of thetip voltage. This technique is, for instance, useful to visualize the localfield enhancement behavior of ultrasharp tungsten tips used as electronemitters.

8. CONCLUSION

The studies presented in this review demonstrate that SEM in transmis-sion mode is a valuable technique that bridges the gap between SEM andTEM. It combines the versatility of a SEM with the advantageous imagingmodalities using TEs. TSEM can replace TEM or STEM if atomic resolutionis not required, and it offers new imaging possibilities and applicationsdue to the usage of low-energy electrons.

Although the TSEM technique has been described and applied sincethe early days of SEM, it was rather a side issue and gained increas-ing interest especially in the past decade when high-resolution SEMsand reliable target preparation techniques using FIB became increasingly


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available. Besides the lower cost, the ease of handling and the high sam-ple throughput of TSEM compared with TEM have encouraged its furtherdissemination. Today TSEM does not intend to rival TEM and STEM interms of achieving highest resolution, but it is a good choice to reduceworkload by taking over less demanding tasks.

Since scattering cross sections of low-energy electrons are large, whichis advantageous especially for low-Z materials, TSEM yields high-contrastimages of, for example, polymers or biological samples. For instance, it ispossible to distinguish between different components in polymer blendsor to study biological samples without metal staining. Moreover, usingTSEM in an environmental SEM enables the investigation of suspen-sions in their wet environment and allows the study of dynamic colloidprocesses at the nanoscale.

TSEM has strong potential particularly for the investigation ofnanoparticles since they are nanoscaled by nature and hence do notrequire elaborate preparation techniques. It is possible to carry out accu-rate and traceable measurements of nanoparticle size and shape. Theaccuracy of these measurements benefits from the fact that TSEM imagescan be reliably modeled and simulated in acceptable time using moderncomputers, thus enabling quantitative comparisons between experimentand theory. In addition, due to the strong correlation between TSEM sig-nal and sample thickness, TSEM is able to quantitatively measure filmthickness with lateral resolutions at the nanoscale.

The broad range of applications that are already visible today demon-strate that TSEM is a versatile technique that presumably will be usedwith increased frequency and might become a standard method—forexample, in material science and biology. Further developments in SEMtechnology, such as continuing improvement of lateral resolution downto the subnanometer range, will shorten the distance to TEM regardingresolution and might make TSEM a serious competitor of high-voltageinstruments. Hence, the affirmative answer to the rhetoric question “IsSTEM possible in a SEM?” given by Joy and Maher (1976) stating thatTSEM will be “a versatile instrument for many applications” is still valid.Many of the applications they had in mind have been demonstrated,and further applications of TSEM will most probably become possible infuture.

LIST OF ABBREVIATIONS

1D One-dimensional2D Two-dimensional3D Three-dimensionalBF Bright-fieldBSE Backscattered electron


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CCD Charge-coupled deviceCSDA Continuous slowing-down approximationDF Dark-fieldDLS Dynamic light scatteringEDX Energy-dispersive X-ray spectroscopyEELS Electron energy-loss spectroscopyESEM Environmental Scanning Electron Microscope (Microscopy)FIB Focused ion beam (milling)GUM Guide to the expression of uncertainty in measurementHAADF High-angle annular dark-fieldIC Integrated circuitMCSEM Monte Carlo Simulation of Electron MicroscopyMONSEL MONte carlo Simulation of secondary ELectronsNIST National Institute of Standards and TechnologyPE Primary electronPTB Physikalisch-Technische BundesanstaltROI Region of interestSAXS Small-angle X-ray scatteringSCALPEL SCattering with Angular Limitation Projection Electron

beam LithographySE Secondary electronSE1 Secondary electron (excited by PE)SE2 Secondary electron (excited by BSE in the sample)SE3 Secondary electron (excited by BSE in the vacuum chamber)SEM Scanning electron microscope (microscopy)SNR Signal-to-noise ratioSTEM Scanning transmission electron microscope (microscopy)STM Scanning tunneling microscope (microscopy)TE Transmitted electronTEM Transmission electron microscope (microscopy)TSEM Transmission scanning electron microscope (microscopy),

SEM in transmission modeZ (low-Z) (Material having a low) atomic number

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