Improvement of the Correlative AFM and ToF-SIMS Approach Using

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Improvement of the correlative AFM and ToF-SIMS approach using an empirical sputter model for 3D chemical characterization Tanguy Terlier, Jihye Lee, Kang-Bong Lee, and Yeonhee Lee Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b03431 • Publication Date (Web): 22 Dec 2017 Downloaded from http://pubs.acs.org on December 30, 2017

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Analytical Chemistry

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Improvement of the correlative AFM and ToF-SIMS approach using an empirical sputter model for 3D chemical characterization

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T. Terlier1, J. Lee1, K. Lee2, and Y. Lee1*

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Advanced Analysis Center, Korea Institute of Science & Technology, Seoul 02792, Korea.

Green City Technology Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea

*Email: [email protected]

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1 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract:

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Technological progress has spurred the development of increasingly sophisticated analytical devices. The full characterization of structures in terms of sample volume and composition is now highly complex. Here, a highly improved solution for 3D characterization of samples, based on an advanced method for 3D data correction, is proposed. Traditionally, Secondary Ion Mass Spectrometry (SIMS) provides the chemical distribution of sample surfaces. Combining successive sputtering with 2D surface projections enables a 3D volume render to be generated. However, surface topography can distort the volume render by necessitating the projection of a nonflat surface onto a planar image. Moreover, the sputtering is highly dependent on the probed material. Local variation of composition affects the sputter yield and the beam-induced roughness, which in turn alters the 3D render. To circumvent these drawbacks, the correlation of the Atomic Force Microscopy (AFM) with SIMS has been proposed in previous studies as a solution for the 3D chemical characterization. To extend the applicability of this approach, we have developed a methodology using AFM–Time-of-flight (ToF)-SIMS combined with an empirical sputter model – “dynamic model-based volume correction” – to universally correct 3D structures. First, the simulation of 3D structures highlighted the great advantages of this new approach compared with classical methods. Then, we explored the applicability of this new correction to two types of samples, a patterned metallic multilayer and a diblock copolymer film presenting surface asperities. In both cases, the dynamic model-based volume correction produced an accurate 3D reconstruction of the sample volume and composition. The combination of AFM–SIMS with the dynamic model-based volume correction improves the understanding of the surface characteristics. Beyond the useful 3D chemical information provided by dynamic model-based volume correction, the approach permits us to enhance the correlation of chemical information from spectroscopic techniques with the physical properties obtained by AFM.

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Keywords: AFM, ToF-SIMS, 3D correction, sputter model

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Since more than twenty years, technological progress in both material sciences and life sciences has been marked by extensive miniaturization of devices and a wide diversification of the structures that can be studied1-5. Although the characterization techniques are being continually improved, no single technique can provide sufficient information to fully understand a given material or bio-sample. Moreover, the increased complexity of the samples has led to an evolution of conventional analysis through the combination of multiple analytical methods. For example, the correlative approach, which has attracted great interest for performing multimodal characterization, allows access to functional information based on the measurement of local chemical and physical properties combined with complementary techniques6. Traditionally, this method combines scanning electron microscopy, which provides high-resolution information down to the nano-scale, with fluorescent imaging, which highlights the individual regions as a function of their chemical composition7. Recently, other pairs of techniques have also been coupled, such as Atom Probe Tomography (APT) with Electron Tomography8, X-Ray Tomography with Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS)9 or Atomic Force Microscopy (AFM) with Raman Microscopy10. In comparison with most existing characterization techniques, AFM offers a large range of measurable signals for the mechanical, magnetic or electrical properties of the sample surface while also providing topographical information with lateral and vertical resolution close to the atomic scale11. Nevertheless, AFM cannot be considered as a chemical, but only as a physical analysis tool, because the complex interactions between the sample and tip depend on various factors such as the sample morphology, the surface composition and the tip shape12. By contrast, various chemical analytical techniques permit the detailed characterization of the chemical composition, both elemental and molecular, with a high chemical sensitivity. In particular, ToF-SIMS13 is considered as a versatile technique thanks to the evolution of ion beam technology, which has broadened the range of practical applications for ToF-SIMS imaging14. The recent development of cluster ion sources, including Bi3+, C60+ and Arn+, offers submicron resolution and simultaneous collection of all masses in the scanning ion image15-17. Moreover, this technique has an excellent chemical sensitivity above 1 ppm and also an acceptable lateral resolution under 1 µm for investigation at the micro- and nano- scales16,18. Using a sputter gun, ToF-SIMS can produce a multi-slice stack giving a 3D render of all chemical species recorded during the analysis14,19. Besides the increased complexity of the structures, the use of multiple materials and the 3D architecture make 3D material characterization increasingly challenging, in particular if the materials have different sputter yields or if the sample presents a surface topography20.

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Recently, a correlative approach combining AFM and SIMS was developed for accurate 3D compositional analysis21-23. This method is less susceptible to several important artifacts in conventional 3D image reconstruction during the calculation of the volume from a flat sample surface23. Even when the samples naturally exhibit a flat surface topography, their surface morphology can be modified by ion beam bombardment. The sputtering process is greatly influenced by the material properties and the local angle of incidence of the ion beam24. For samples composed of more than one material, preferential sputtering phenomena frequently occur. Therefore, the 3D render is highly sensitive to the sputtering process, which can cause distortions in the 3D maps of the sample and misinterpretation of the data25,26. The in-situ combination of AFM and SIMS in a single instrument was presented by two groups as an attractive and pioneering solution to improve the 3D reconstruction and to monitor the evolution of the local sputter depth21,23. However, the combined instrumentation suffers from long analysis times as a result of the sequential characterization by alternating SIMS and AFM measurements 3 ACS Paragon Plus Environment


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if numerous scans of the topography are required. And it cannot be well-accessed for routine analysis because it is specially designed instrument for the correlative AFM–SIMS. In parallel to solutions based on instrument modification, other approaches using improved data treatment have been proposed. Originally described by Breitenstein et al.27, a 3D correction of the ToFSIMS data was demonstrated in practice by Graham’s group20. This method assumes two major hypotheses: that the cyclic sputtering is analogous to the peeling of an onion, and that the sputter rate is constant both throughout the 3D depth profiling and in the entire analyzed volume. Given these assumptions, the 2D contour surface between the 3D object and the substrate is detected and is then defined as zero, which flattens the surface of the substrate and corrects for shifting of the voxel locations in term of their Z-positions20. To apply this correction, it is necessary to determine the validity of these assumptions. In reality, the sputtering phenomenon involves many complex processes that, as previously noted, can generate preferential sputtering and induce ionbeam damage28,29.

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In this work, we submit a new method to correct the 3D images from surface analysis, named the “Dynamic model-based volume correction” method. This approach associates AFM measurements of sample topography from the surface and from the crater with SIMS analysis. A sputter model is also integrated to calculate the evolution of the topography during the 3D depth profiling. The aim of this paper is to explore the analytical potential of this advanced approach by comparing the 3D image reconstructions of various simulated structures obtained (a) by AFM measurements alone, (b) with correction by the contrast threshold of the interface and (c) with the dynamic model-based volume correction. Furthermore, we demonstrate the features of this approach by presenting two real cases in the field of material science.

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Experimental section:

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Materials & sample preparation Figure 1

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Inorganic patterned grid microstructure

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A patterned multilayer was obtained using a copper grid (G300F1 from Electron Microscopy Sciences, Hatfield, PA, USA). The 300-mesh grid was attached on a silicon substrate by a metal tape with a circular hole, allowing the grid to be fixed like a mask.

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Coating layers of platinum and gold were deposited using a Hitachi E-1045 Ion Sputter instrument (Hitachi Inc, Tokyo, Japan) at 15 mA and an Eiko ib 3 Ion Sputter instrument (EIKO Engineering Co., Ltd, Japan) at 3 mA, respectively. The composition of the multilayer structure, shown in Figure 1.a, was the following: Si (substrate)/Pt (20 nm)/Au (10 nm)/Pt (20 nm)/Au (15 nm)/Pt (20 nm). Each layer was measured by a stylus profiler following deposition.

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dPS-b-PMMA diblock copolymer film

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A diblock copolymer (BCP) of deuterated polystyrene (dPS) – poly(methyl methacrylate) (PMMA), denoted dPS-b-PMMA, with a number-average molecular weight of Mn = 37,600 (MdPS = 19,500 and MPMMA = 18,100, polydispersity index = 1.06), was purchased from Polymer Source, Inc (Dorval, Canada).


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Prior to spin-coating, square silicon wafers (0.5 mm thick, with side lengths of 20 mm) were sonicated in acetone and methanol. A polymer solution was created by dissolving 15 mg of dPS-b-PMMA in 1 mL of toluene. Then, 0.5 mL of the polymer solution was dropped onto the silicon wafer for spin-casting (2000 rpm, 60 s). Then, the polymer film was annealed at 190 °C for 24 h in a vacuum oven.

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As reported by Huang et al.30, the block organization can be symmetric or asymmetric as a function of the film thickness, h, and the block periodicity, L0. On the basis of previous observations by Kang31 and a preliminary analysis of this film by AFM, the film structure was determined to consist of alternated lamellar blocks of PMMA and dPS with antisymmetric boundary conditions, i.e., having a film thickness corresponding to h = (n + 1/2)L0 (where n is an integer). Moreover, the presence of surface instabilities during the phase separation induced the formation of holes throughout the entire depth of the film thickness32. Figure 1.b shows a 3D view of the BCP, in which the following film structure was observed: Si (substrate)/PMMA (7 nm)/dPS (14 nm)/PMMA (14 nm)/dPS (14 nm)/PMMA (14 nm)/dPS (5-7 nm), with an estimated period L0 equal to 28 nm.

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Characterization methods

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Stylus profiler

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The film thicknesses were measured by a TENCOR P-10 Surface Profiler (KLATENCOR, Milpitas, CA, USA) using a stylus force of 20 mg to scan a line of 500 µm at 20 µm/s with a frequency of 50 Hz.

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Atomic force microscopy

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AFM analysis of the structure was performed under ambient conditions with an XE-100 microscope (Park Systems Instrument, Suwon, South Korea) to examine the topography of the surfaces of the samples and in the ToF-SIMS crater after sputtering. A mosaic image (3 × 3 squares) of size 60 µm × 60 µm was obtained in tapping mode.

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Similarly, AFM analysis of the BCP film was performed in tapping mode using a dimension edge microscope (Bruker Korea Co., Ltd, Seoul, South Korea) to scan a square of size 60 µm × 60 µm.

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ToF-SIMS analysis

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ToF-SIMS analyses were performed using a ToF.SIMS5 (ION-TOF GmbH, Münster, Germany) operated in dual-beam configuration. All surface chemical analyses were obtained using a 25 keV Bi3+ analysis beam with a cycle time of 100 µs.

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Images of the inorganic sample were acquired using 2048 × 2048 pixels and a raster area of 120 µm × 120 µm. The images were obtained using a binning factor of 64 to improve the image visual quality by reducing noise. During the static analyses, the conditions were optimized using an electron flood gun for charge compensation. For the 3D chemical analyses, a sputter gun was used. A series of sequential images was acquired using a 1 keV Cs+ sputtering beam (typically 15 nA) and a 25 keV Bi3+ analysis beam (typically 0.2 pA). The raster areas were 400 µm × 400 µm for sputtering and 120 µm × 120 µm for analysis. The beams were operated in a



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non-interlaced mode using the following operational cycle: 1 analysis cycle, followed by a 5 s sputtering cycle, then a pause of 2 s for the inorganic sample.

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For the BCP sample, the raster size and the analyzed area were respectively, 300 µm × 300 µm and 60 µm × 60 µm (with 256 × 256 pixels per image). To limit the accumulation of damage and the loss of molecular information, the operational cycle consisted of 2 analysis cycles and 15 s of sputtering.

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In both measurements, the electron flood gun was turned on during the 2 s pause to compensate for the charges that may have accumulated at the top surface. Negative secondary ions in the range of m/z = 0–912 (fixed cycle time of 100 µs) were analyzed, allowing the characteristic ions to be monitored.

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3D reconstruction method

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After peak identification of the ToF-SIMS data, ion images were obtained from peaks known to correspond to the components of the sample materials using IONTOF SurfaceLab v.6.6 software. Then, the dataset was exported as XYZI files and saved in a folder. Those files were compiled using the MATLAB program (Mathworks, Natick, MA, USA). Firstly, the XYZI files were converted into a 3D TIFF Virtual stack using a binning with a factor of 64 by the “sum” method. Then, several methods of 3D correction were applied to the ToF-SIMS dataset. The detailed theory and simulation procedures of 3D correction can be found in the supporting information (Figures S1-S3).

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To apply 3D correction, several data pretreatments are required. The first is to correct the lateral shifting of the ToF-SIMS fields of view during the sputtering. The lateral shifting of the analysis area affects the 3D render of the object and can disturb the data interpretation. To correct this, the matching approach for slice alignment aims to minimize the misalignment deviation using the normalized cross-correlation coefficient. The translation vector giving the maximal normalized cross-correlation coefficient is considered as the best image position. Thus, the optimization of the lateral shift correction permits the reshaping of the object in the original render.

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Then, when AFM data are also used to correct the 3D ToF-SIMS dataset, an analogous method can be used but including all 2D planar transformations, such as similarity and affine transformation, to define the common areas of both analyses so that the same region of interest (RoI) can be selected for both.

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After the pretreatment for the alignment and the shift correction of the data, several methods were applied to calculate the Z-voxel positions.

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The first method was to use the static topographic information obtained by AFM to correct the voxel positions33. Each voxel of the ToF-SIMS dataset was calculated from the topographic value of the same pixel position from the AFM surface analysis. The distances between two successive slices were determined by the ratio of the extrema of the surface topography and the interface contour. In cases where the topographic information of the surface and from the crater bottom were both available, the distance was adjusted for each pixel position by the difference between the two AFM images.


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The second method was to detect the interface position of each XY pixel in the ToF-SIMS dataset between the substrate and the top surface of the object. Considering that the substrate surface is flattened, the entire stack was Z-shifted by a distance corresponding to that between the interface location and a given pixel. This method was previously presented by Robinson et al.20. In our case, the threshold value was fixed to 84 percent of the maximum substrate intensity (corresponding to the definition of an interface for the depth profiling).

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The third method was our 3D correction method, the dynamic model-based volume correction, based on dynamic topographic modelling, i.e. using the simulation of the evolution of the surface topography after each sputter cycle. This approach is virtually similar to the acquisition of the surface topography in in-situ AFM–SIMS instrumentation. The model is an empirical model based on the measurements of both the sputtering rate and the beam-induced roughness of the reference materials (present in the analyzed volume), which are used to measure the local evolution of the topography. The chemical nature of the local voxel is determined by segmentation of the 3D PCA images. The distance between two successive voxels are calculated using the equation 1. Combining both the above methods, this approach produces associations between various sources of statistical information from the sputtering, such as the sputter yields of the materials and the beam-induced roughness, to calculate accurate voxel positions.

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The initial step consisted of scanning the surface topography of the RoI by AFM before ToF-SIMS analysis (Figure S4.a) which can be performed using an ex-situ AFM instrument or in an in-situ AFM–SIMS instrument. Then, contrast intensity thresholding was applied to slice the voxels, starting from the region at the top of the substrate, using the interface contour with the substrate (Figure S4.b). The first slice was corrected using the values of the surface topography for each pixel (Figure S4.c). To adjust the voxel positions, a geometrical model of the sputtering was run. This model incorporates both the sputter yield and the roughness induced by sputtering for each material present in the sample.

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The 3D locations of each phase were obtained using principal component analysis (PCA). With PCA discrimination, any architecture type can be corrected (Figure S5.a). Each voxel can be considered to represent any material type, including pure or mixed material (alloy, doped material …). As reported by Wagner et al.34, the ability to discriminate between materials requires a special data pretreatment to assign a weight to each voxel intensity before the PCA and to decrease the signal-to-noise ratio and the influence of each material’s ion yield. Here, the pretreatment was performed by normalizing each component of the stack to the maximum intensity for that component. The weighting of the pixels was determined by the percentage of the normalized intensity for each component with respect to the sum of the normalized intensities. To complete the pretreatment, the data were mean-centered, allowing greater discrimination of the signal from the noise. Then, the principal components were segmented into domain maps. The domain maps were generated by assigning the threshold score of zero to the PCA factors for each image along the stack35. Then, each component was recombined to obtain a 3D mask. Based on the global approach to defining the depth scale in the depth profile presented by Wagner et al.36, a modified formula was applied to local voxels, which integrated the beaminduced roughness. Corrective factors were also used to locally adjust the sputter rate to correct for the effect of the 3D topography on the sputter yield. Thus, the corrected distance between two successive voxels having the same XY position was calculated by the following equation:



௡

∆ܼ௜,௝ = ෍ ቀܴܵ௜,௝ ௠௔௧ୀଵ

௠௔௧

− ܴ݉‫ݏ‬௜,௝

௠௔௧

ቁ . ‫ܥ‬௜,௝ . ‫ܫ‬௜,௝

௠௔௧

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(Eq. 1)

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where i and j correspond, respectively, to the pixel number of the X and Y position, ܴܵ݅,݆ is ݉ܽ‫ݐ‬ the measured sputter rate, ܴ݉‫݅ݏ‬,݆ ݉ܽ‫ ݐ‬is the beam-induced roughness for the corresponding material. Simulated values of the sputter rate and the beam-induced roughness were obtained using normal distributions. The measured sputter rate and the measured roughness are considered as the mean of the distributions. The standard deviations of both parameters were extracted using image analysis of the AFM images of reference samples, observing the variations before and after sputtering. ‫݅ܫ‬,݆ ݉ܽ‫ ݐ‬is the local relative composition value obtained for each material after normalization and PCA. ‫݅ܥ‬,݆ is the local corrective factor to adjust the variation of the sputter rates as a function of the surface topography, calculated using the ratio between the surface topography obtained by AFM and the interfacial surface detected by the material/substrate threshold. This local corrective factor is mainly used to consider the role of the local impact angle of the ion beam, which modifies the sputter rate as a function of the regions with different angle slopes.

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Then, the 3D matrix of new voxel positions (Figure S5.b) was applied to the data stack for each selected signal, the output of which was displayed in 3D view.

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Results & discussion:

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The signal detected from an object is affected by both its 3D structuration and the presence of surface topographical features, which in turn influence the final rendered image. During 3D depth profiling, successive 2D mapping projections of the investigated surface are acquired from cyclic sputtering. The combination of all 2D projections gives a virtual stack, which provides a 3D volume render of the dataset. However, the 3D render is built as a cube-like volume, whereas the sample surface is flat. This configuration produces an inverted topography of the targeted object. The 2D surface projection affects, in particular, the depth profile, which modifies the interpretation of the in-depth distribution. Moreover, analytical artifacts, such as charge effects, beam-induced roughness, mass interference and the effects of complicated topography, can also influence the 3D analysis. For example, the use of ToF-SIMS analysis with high lateral resolution reduces the mass resolution, which increases the probability of mass interference. An accurate 3D image allows the deconvolution of the spectrum as a function of the height position to identify specific information.

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To obtain the most accurate 3D chemical characterization, the in-situ AFM–SIMS combination is a particularly well-adapted approach. The use of a standard ultra-high vacuum (UHV) chamber permits the sample surface to be protected against the influence of air exposure. Indeed, the exposure of the sample after sputtering in ambient atmosphere can generate contaminations or surface modifications of the sample because of the surface sputtering itself or the implantation of ions in the analysis zone. In particular for the case of Cs+ ion sputtering, the contact between the crater of the sample and the air can create a high density of cesium oxide aggregations37. In the combined instrumentation, the initial topography of the sample surface as well as topographic changes can be easily observed during the experiment. The interlacing approach ensures precise correlation between the SIMS and the AFM measurements. The dynamic model-based volume correction can be applied to any type of instrumentation. For

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example, ex-situ AFM can be combined with a SIMS instrument. In this case, the influence of the air exposure on the sample is crucial. However, by adjusting the number of sputter cycles and the sputter rate ratio38 in the dual beam mode of ToF-SIMS, the implantation of cesium can be limited. This reduces the presence of cesium on the surface, which would otherwise modify the topography when the sample is exposed to air after the sputtering39. Thus, the protocol requires only one or two measurements of the topography by AFM.

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Although the in-situ SIMS–AFM combination offer the most accurate approach for the 3D characterization, the estimated time for moving and recording the surface scan is about four minutes (in the IONTOF ToF-SIMS system) for each slice. Using our method, the dynamic model-based volume correction can be applied to any type of instrumentation. For example, exsitu AFM can be combined with a SIMS instrument, as presented in this paper. The ex-situ AFM analysis is currently about 20 min and the sample transfer under UHV is around 15 min. The data reconstruction of the 3D render is calculated in 5 min. For a typical volume render of 100 slices, the timing of ex-situ AFM combined with SIMS using the dynamic model-based volume correction is about 60 min. compared with around 400 min for the in-situ SIMS–AFM instrument. Moreover, the dynamic model-based volume correction can instead be complementary to the in-situ SIMS–AFM instrument, it both are available. We estimate that the time can be considerably reduced to as little as 15 min. if our method is applied complementarily with the combined instrumentation.

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Simulation of 3D correction

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To evaluate the strengths and limitations of the 3D reconstruction methods, various types of structure were simulated to examine their compatibility with the methods.

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Figure 2

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Figure 2 displays the cross-sectional view of the structures investigated to compare the reconstruction methods. To study all of the possible artifacts that can affect 3D reconstruction using the various methods, it is necessary to simulate a wide variety of structures. Among the chosen structures, the bulk (Figure 2.a) is the simplest case. This example permits us to check the ability of each method to reproduce the original shape and 3D distribution without considering differences in sputtering caused by the different materials. The second case is a multilayered structure (Figure 2.b) which is most pertinent to studying the effects on the depth resolution or the influence of the different sputter yields. In the Si/B/A/B/A structure, material A (colored red) alternates with material B (colored blue). The third example, the core-shell structure, is complementary to the multilayered structure, in that its organization is not planar but curved, as shown in Figure 2.c. The core is composed of a layer of material B (colored blue), coated by shell consisting of a layer of material A (colored red). The final case was selected to examine how a sublayer, in this case an interfacial layer (material B, blue) between the 3D object (material A, red) and the substrate, is affected when this region is not 3D structured, and how it is altered by a 3D object present in the top layer (Figure 2.d).

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The sputter process of the top surface can be considered as analogous to the peeling of an onion, in which the sputter yield and the beam-induced roughness depend on the material. Based on this concept, simulations were performed to reproduce the 3D render as a function of different configurations. The results are displayed in the supporting information in Figure S6. As 9 ACS Paragon Plus Environment


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expected, the 3D render of the bulk structure shows an inversion of the topography. The interface appears rougher than in the original, pre-sputtering sample, which implies the accumulation of beam-induced roughness during the sputter process. Moreover, the comparison between the surface topography and the topography obtained after the volume analysis reveals a “memory effect”, as presented in Figure S7. This “memory” effect is visible for all of the structures and depends on the original topography and the difference of sputter yields. Other than the bulk, the remaining structures are composed of two materials in the top layer. The sputter rate and the beam-induced roughness of each material calculated by our simulations are reported in Table S1.

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Qualitatively, the cross-sectional visualization of the structured layers permits us to identify how the 3D render derived from the surface analysis can distort the data interpretation. The depth profiles obtained during the simulations are reported in Figure S8. In the case of the multilayered structure, the layers are curved because of the multiple projections. The depth profile resulting from the simulation of the multilayered structure shows an interlacement of the signals along the depth. The low variation of the signals results in poor definition of the regions where the layers have a pure material composition. Similarly, the core-shell structure is also disfigured by the surface analysis. In particular, the volume of the core is greatly reduced because of the higher sputter yield of material B than that of material A. The render obtained for an interfacial layer is also altered, which may limit the interpretation of the interface. Moreover, the depth profile shows the presence of a thin layer on the substrate, while another region is also visible at greater depth. The interpretation of the profile is made impossible by the broadened distribution of the characteristic signal of the interfacial layer (material B).

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As described earlier, several reconstruction methods were applied to correct the artifacts of 3D structuration, and the quality of each method for the various structures was compared.

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Figure 3

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Figure 3 shows the cross-sectional views of the 3D renders obtained after reconstruction using (a) the AFM topography before and after sputtering (Figure 3.a), (b) the contrast intensity threshold method (Figure 3.b) and (c) the dynamic model-based volume correction (Figure 3.c) on the bulk half-bead. In the first case, the use of AFM surface topography data permits us to obtain accurate surface information, which can be correlated with the first ToF-SIMS analysis slice. However, for both the original surface and crater surface, the 3D reconstruction is distorted by the difference of the sputter yields between the material and the substrate. In particular, the interface position beneath the top object is altered, as shown in Figure 3.a. In the second case, the flattening of the substrate interface gives a well-corrected 3D reconstruction of the top object using the contrast intensity threshold method. However, although the interface position is properly remodeled, the 3D volume is not to scale and the surface reveals the accumulation of beam-induced damage caused by the sputtering prior to reaching the interface, as depicted by Figure 3.b. The final method, dynamic model-based volume correction, combines the advantages of both previous methods by integrating the AFM data and an empirical sputter model. In this case, the surface is obtained using the AFM surface topography. The flattening of the substrate permits the reshaping of the object volume marked in red. The craters are also analyzed, which is crucial to adjusting the substrate volume. Nevertheless, a low density of voxels remain empty because of the roughness generated by the beam-induced damage along the depth (Figure 3.c). 10 ACS Paragon Plus Environment

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The comparison above has highlighted the limitations of the method using only static AFM topographic data for 3D reconstruction of the bulk half-bead. By contrast, the flattening of the substrate interface permits the reshaping of the top part of the sample. To further explore the limitations and advantages of the methods in which the substrate interface is flattened, these reconstruction methods were applied to the multilayered half-bead.

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Figure 4

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The depth profiles and the cross-sectional views of the multilayered half-bead obtained after reconstruction are displayed in Figure 4.. The retro-projection of the 3D volume obtained using the contrast intensity threshold method (Figure 4.a.) demonstrates the preservation of the Si/B/A/B/A multilayer structure. Nevertheless, the layers appear to be affected by the beaminduced roughness, which alters the interfaces. In particular, this appears to degrade the depth resolution along the depth profile. The selectivity of the sputtering also influences the depth scale of the layers, resulting in poor thickness definition. Figure 4.b shows the results obtained when the dynamic model-based volume correction is applied to the multilayered half-bead. The multilayer structure is conserved using this method. By contrast with the previous method, the interfaces are well-defined. The beam-induced roughness is greatly reduced but a certain number of voxels is empty after reconstruction because of the failure to probe a small fraction of the volume during the sputter process. That is, during the sputtering, the surface was eroded at a constant sputter rate, producing a beam-induced roughness. Thus, some small volumes were not analyzed between successive sputter cycles. With this method, it is possible to visualize the density of these neglected volumes while also improving the interpretation of the signal changes along the depth profile. In that profile, the red signal is more intense than the blue signal. This difference is mainly caused by the beam-induced roughness generating empty voxels. In particular, the density of empty voxels is increased in the material B because of a higher accumulation of the beam-induced roughness.

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To test the generality of the above observations, the same reconstruction methods were applied to a core-shell structure. In contrast with a multilayer, the organization of such structures is not planar but radial, which can be challenging to correct. Figure 4.c and Figure 4.d compares the results of the two methods. For the contrast intensity threshold method (Figure 4.c), the limit of this approach is apparent from the surface topography at the top of the shell region. The sum of the beam-induced roughness detected at the interface is retro-projected onto the surface of the object, which distorts the 3D render and gives rise to a misinterpretation of the core-shell structure. By contrast, the dynamic model-based volume correction method (Figure 4.d) can give an accurate correction of this structure because of its ability to regulate the beam-induced roughness along the depth.

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In its reconstruction of the multilayered and core-shell structures, dynamic model-based volume correction demonstrated its ability to correct the 3D render to minimize the artifacts affecting the analysis when a 3D object is present on the surface of the substrate. To complete the evaluation of this method, the final example considered the case where an interfacial layer is present between the substrate and a 3D object on the surface. As expected, the 3D object (Figure 4.e) in the presence of an interfacial layer was accurately reconstructed. In this example, the result of the contrast intensity threshold method is not discussed because the limitations of this method are similar to those previously discussed for reshaping the bulk structure. 11 ACS Paragon Plus Environment


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The combination of topographic information from AFM measurement with an explicit model of the sputtering process has shown real potential as a method to reconstruct the original structures of simulated 3D architectures. To demonstrate the utility of this method, two real cases were investigated: a metallic multilayered structure with a W-shaped pattern and a PS-b-PMMA diblock copolymer film organized in a lamellar structure. 3D correction of metallic multilayer sample

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Beyond the 3D analysis of the multilayer structure, performing data correlation between the AFM topography and the ToF-SIMS chemical mapping from the sample surface can also improve the interpretation of the observation. In the present case, the combined approach permits us to validate the accuracy of the alignment of the two images in which the datasets are pretreated using geometrical transformation, as shown in Figure S9.

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Figure S9.a gives the chemical composition of the sample surface before sputtering. From this mapping and using the AFM topography (Figure S9.b), the correlated AFM/ToF-SIMS image is displayed in Figure S9.c. This image permits us to check the quality of the alignment which is the preliminary step to reconstructing the 3D volume of the sample. With the correlative approach, it is also possible to interpret the spatial distribution of the elements. Here, the gold signal is concentrated on the sidewall of the patterned structure while the top of the pattern is mainly composed of platinum, corresponding to the last deposited layer.

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The field of view (FoV) of the AFM image, about 75 µm, does not cover all of the RoI from the ToF-SIMS. For this reason, the dataset was cropped into the FoV of the AFM to apply the 3D-reconstruction method. The results obtained in ToF-SIMS are shown in Figure S10. The 3D render, Figure S10.a, displays an inversion of the topography relative to the AFM topography. The XZ image of the analyzed volume exhibits the multilayer structure and also confirms the distortion of the elemental distribution in this volume (Figure S10.b). The depth profile of this RoI does not permit any clear interpretation because of the interlacement of the signal along the depth, as represented in Figure S10.c. Only the pure silicon substrate region is identifiable.

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Then, the sputter rates and the beam-induced roughness were measured in a bulk film of each material to apply these as simulation parameters, as reported in Table S2.

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Figure 5

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Figure 5 shows the results of the 3D reconstruction using the dynamic model-based volume correction. The 3D image, shown in Figure 5.a, is in agreement with the previous observation of a significant concentration of gold on the sidewalls of the pattern. The XZ image (Figure 5.b) exhibits a well-reconstructed multilayer structure. Nevertheless, an interfacial region of 10–15 nm is visible. This effect has two main causes: the patching of the AFM images, which causes the positions of the background to be shifted by a few nanometers during the combination of the images; and the imperfection of the sputter model. Indeed, the topography can affect the local sputter yield, which distorts the onion-peeling process assumed by the model. The local corrective factor applied in the simulation was able to limit this effect but did not suppress completely the influence of this phenomenon on the 3D volume. Despite this residual artifact, a depth profile was extracted from this volume and is displayed in Figure 5.c. By contrast with the 12 ACS Paragon Plus Environment

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depth profile obtained from the raw data, the material and the substrate regions are well-defined, which permits us to measure the approximate thickness of the layers. Table 1

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The exact values could not be calculated because of the inhomogeneity of the layer deposition in the pattern region, but these values were compared with the film thicknesses obtained from a sample with full-sheet deposition under the same coating conditions. Table 1 summarizes the thickness measurements. As can be seen, the thickness values extracted in the 3D reconstructed volume show a very good agreement with the values obtained by profilometry in the full-sheet sample.

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Although the 3D reconstruction of the multilayered pattern was a success, another type of organic structure was tested to explore the utility of this new method for real applications.

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3D correction of dPS-b-PMMA diblock copolymer

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By contrast with the previous example, the diblock copolymer film has the specific ability to self-assemble into micro- or nano-sized objects during the phase-separation of the two polymers40. The film organization depends on the molecular weight ratio between the polymers40. In the present case, the dPS-b-PMMA diblock copolymer film is structured in lamellar layers alternating between deuterated PS and PMMA layers.

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The original data obtained by ToF-SIMS are shown in Figure S11. The 3D render, Figure S11.a, does not display the surface asperities visible in the AFM topography. The cross-sectional image and the depth profile, respectively Figure S11.b and Figure S11.c, show the dPS/PMMA multilayer structure. However, both signals are relatively closely interlaced along the depth, which limits the interpretation of the lamella thickness and the depth distribution of the polymers.

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The preliminary observations revealed an inhomogeneous surface with a high density of approximately hole-shaped circular asperities31. AFM measurements (Figure S12.a) were performed in this RoI before and after the 3D ToF-SIMS depth profiling. The first image of the ToF-SIMS stack (Figure S12.b) was compared with the AFM image. The comparison of the hole regions reveals the presence of PMMA in the “double hole” region and of dPS in the “single hole” region on the top film (Figure S12.c).

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After the alignment of the AFM image with the stack of ToF-SIMS images, a re-alignment of the ToF-SIMS images was required. Indeed, the visualization of the stack demonstrated a shifting of the FoV along the depth. The quality of the shift correction was controlled using the interfacial images before (Figure S13.a) and after correction (Figure S13.b) and compared with the AFM image (Figure S13.c). The resulting image in Figure S13.b is clearly very similar to the AFM image, which confirms that the shift correction was well chosen.

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Then, to establish the dimensions of the film and the sputtered volume, the AFM results were coupled with the crater depths measured using a stylus profiler. A diagram of the resulting film structure is displayed in Figure S14.



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Next, to de-interlace the polymer signals, segmentation was performed using a data pretreatment and applying PCA to each image in the stack. Then, the PCA results were separated as a function of the principal components in the 3D domain maps, as shown in Figure S15.

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Then, the sputter rates and the beam-induced roughness were measured in a bulk film of each material to apply these as simulation parameters, as reported in Table S3.

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Figure 6

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Figure 6 shows the 3D reconstruction of the diblock copolymer film, organized in a lamellar structure with hole-shaped asperities. The 3D image (Figure 6.a) and the cross-sectional view (Figure 6.b) are in agreement with the theoretical structure of the dPS-b-PMMA film. Moreover, the depth profile (Figure 6.c) displays the multilayer structure, which permits us to interpret the thickness of each lamella.

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This second example has demonstrated the ability of the method to correct the 3D data in a way that improves the interpretation of the chemical distribution when large asperities are present on the surface of the film.

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Conclusion:

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The 3D reconstruction of ToF-SIMS data was successfully performed using the newly developed dynamic model-based volume correction, combining AFM surface topography and an empirical sputter model. This approach includes a segmentation of the components, allowing the local phases to be discriminated by PCA. Our protocol was compared with two classical methods for correcting 3D data.

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Performing data correlation between AFM and ToF-SIMS has demonstrated a great utility in identifying the chemical composition as a function of topography. However, although the use of the static topography in reconstruction of the 3D volume allows an accurate correction of the surface structure, the depth distribution is distorted at the interface because of the beam-induced roughness and the variation of the sputter yield.

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The contrast threshold intensity method is particularly well-adapted to detection of the interface contour when the object is distinct from the substrate. However, the assumption of a homogeneous sputter yield in the analyzed volume can limit the accuracy of the 3D reconstruction. This method is sensitive to the roughness caused by the ion bombardment, which is transferred to the reconstructed surface.

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To explore the features of our method, two different real cases were investigated with the new protocol. The first was a metallic multilayer sample. The reconstruction revealed the deposition of the metal coating in the form of a core-shell structure, in contradiction to our preliminary assessment. The second example was a dPS-b-PMMA diblock copolymer film organized in a lamellar structure with some hole-shaped asperities. The 3D correction of this organic sample highlighted the depth distribution in the hole regions and also improved the interpretation of the lamella thickness.

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This new method should be helpful both for future technological developments and as a powerful tool to study critical issues in 3D SIMS imaging. The correction obtained with this protocol permits the extraction of more accurate information from 3D-structured samples. Beyond the useful 3D chemical information provided by dynamic model-based volume 14 ACS Paragon Plus Environment

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correction, the approach permits us to enhance the correlation of chemical information from spectroscopic techniques with the physical properties obtained by methods such as AFM.

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Acknowledgements:

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This work was supported by the Korea Ministry of Science and ICT Research Program (Grant No. 2014M3A9E1070337). We thank Leo Holroyd, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.

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References: (1) Fortunato, E.; Barquinha, P.; Martins, R. Advanced Materials 2012, 24, 2945-2986. (2) Allendorf, M. D.; Schwartzberg, A.; Stavila, V.; Talin, A. A. Chemistry – A European Journal 2011, 17, 11372-11388. (3) Koch, N. ChemPhysChem 2007, 8, 1438-1455. (4) Kelley, T. W.; Baude, P. F.; Gerlach, C.; Ender, D. E.; Muyres, D.; Haase, M. A.; Vogel, D. E.; Theiss, S. D. Chemistry of Materials 2004, 16, 4413-4422. (5) Katz, H. E.; Huang, J. Annual Review of Materials Research 2009, 39, 71-92. (6) Wirtz, T.; Philipp, P.; Audinot, J. N.; Dowsett, D.; Eswara, S. Nanotechnology 2015, 26, 434001. (7) de Boer, P.; Hoogenboom, J. P.; Giepmans, B. N. G. Nat Meth 2015, 12, 503-513. (8) Grenier, A.; Duguay, S.; Barnes, J. P.; Serra, R.; Haberfehlner, G.; Cooper, D.; Bertin, F.; Barraud, S.; Audoit, G.; Arnoldi, L.; Cadel, E.; Chabli, A.; Vurpillot, F. Ultramicroscopy 2014, 136, 185-192. (9) Priebe, A.; Goret, G.; Bleuet, P.; Audoit, G.; Laurencin, J.; Barnes, J. P. Journal of Microscopy 2016, 264, 247-251. (10) Asghari-Khiavi, M.; Wood, B. R.; Mechler, A.; Bambery, K. R.; Buckingham, D. W.; Cooke, B. M.; McNaughton, D. Analyst 2010, 135, 525-530. (11) Samori, P. Scanning Probe Microscopies Beyond Imaging: Manipulation of Molecules and Nanostructures; Wiley, 2006. (12) Bonnell, D. Scanning Probe Microscopy and Spectroscopy: Theory, Techniques, and Applications; Wiley, 2000. (13) Vickerman, J. C.; Briggs, D. TOF-SIMS: Materials Analysis by Mass Spectrometry; SurfaceSpectra, 2013. (14) Gilmore, I. S. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 2013, 31, 050819. (15) Stapel, D.; Benninghoven, A. Applied Surface Science 2001, 174, 261-270. (16) Touboul, D.; Kollmer, F.; Niehuis, E.; Brunelle, A.; Laprévote, O. Journal of the American Society for Mass Spectrometry 2005, 16, 1608-1618. (17) Malmberg, P.; Kriegeskotte, C.; Arlinghaus, H. F.; Hagenhoff, B.; Holmgren, J.; Nilsson, M.; Nygren, H. Applied Surface Science 2008, 255, 926-928. (18) Fletcher, J. S.; Rabbani, S.; Henderson, A.; Blenkinsopp, P.; Thompson, S. P.; Lockyer, N. P.; Vickerman, J. C. Analytical Chemistry 2008, 80, 9058-9064. (19) Hutter, H.; Nowikow, K.; Gammer, K. Applied Surface Science 2001, 179, 161-166. (20) Robinson, M. A.; Graham, D. J.; Castner, D. G. Analytical Chemistry 2012, 84, 4880-4885. (21) Bernard, L.; Heier, J.; Paul, W.; Hug, H. J. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 2014, 339, 85-90.

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(22) Koch, S.; Ziegler, G.; Hutter, H. Analytical and Bioanalytical Chemistry 2013, 405, 71617167. (23) Wirtz, T.; Fleming, Y.; Gerard, M.; Gysin, U.; Glatzel, T.; Meyer, E.; Wegmann, U.; Maier, U.; Odriozola, A. H.; Uehli, D. Review of Scientific Instruments 2012, 83, 063702. (24) Mansilla, C.; Philipp, P.; Wirtz, T. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 2011, 269, 905-908. (25) Fleming, Y.; Wirtz, T.; Gysin, U.; Glatzel, T.; Wegmann, U.; Meyer, E.; Maier, U.; Rychen, J. Applied Surface Science 2011, 258, 1322-1327. (26) Nguyen, C. L.; Wirtz, T.; Fleming, Y.; Metson, J. B. Applied Surface Science 2013, 265, 489-494. (27) Breitenstein, D.; Rommel, C. E.; Möllers, R.; Wegener, J.; Hagenhoff, B. Angewandte Chemie International Edition 2007, 46, 5332-5335. (28) Shard, A. G.; Green, F. M.; Brewer, P. J.; Seah, M. P.; Gilmore, I. S. The Journal of Physical Chemistry B 2008, 112, 2596-2605. (29) Wucher, A.; Cheng, J.; Zheng, L.; Willingham, D.; Winograd, N. Applied Surface Science 2008, 255, 984-986. (30) Huang, E.; Pruzinsky, S.; Russell, T. P.; Mays, J.; Hawker, C. J. Macromolecules 1999, 32, 5299-5303. (31) Kang, M.; Lee, J.; Lee, Y. Surface and Interface Analysis 2014, 46, 105-109. (32) Tsui, O. K. C.; Russell, T. P. Polymer Thin Films; World Scientific, 2008. (33) Wirtz, T.; Fleming, Y.; Gysin, U.; Glatzel, T.; Wegmann, U.; Meyer, E.; Maier, U.; Rychen, J. Surface and Interface Analysis 2013, 45, 513-516. (34) Wagner, M. S.; Graham, D. J. Surface Science 2004, Volume 570, 78-97. (35) Lee, J. L. S.; Gilmore, I. S.; Seah, M. P. Surface and Interface Analysis 2008, 40, 1-14. (36) Wagner, M. S. Analytical Chemistry 2004, Volume 77, 911-922. (37) Ngo, K. Q.; Philipp, P.; Kieffer, J.; Wirtz, T. Surface Science 2012, 606, 1244-1251. (38) Grehl, T.; Möllers, R.; Niehuis, E. Applied Surface Science 2003, 203-204, 277-280. (39) Wittmaack, K. Surface Science Reports 2013, 68, 108-230. (40) Tseng, Y.-C.; Darling, S. B. Polymers 2010, Volume 2, 470-489.



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Tables:

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Table 1. Thickness measurements in full-sheet and in patterned region in multilayer structure from the top layer to the substrate

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Pt #1

Au #1

Pt #2

Au #2

Pt #3

5

Full-sheet

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15

22

10

20

6

Pattern

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14

26

11

19

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Figures:

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Figure 1. (a) Schematic of the patterned grid microstructure composed of platinum/gold multilayers coated on silicon substrate. (b) Schematic of the deuterated PS-b-PMMA diblock copolymer film organized in lamellar structure after spin-coating and annealing at 190 °C for 24h on silicon substrate

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Figure 2. Schematic of the simulated half-bead structures on substrate. The structures are (a) bulk, (b) multilayer, (c) core-shell and (d) bulk with interfacial layer. Materials in red and in blue present different sputter yields and beam-induced roughness under ion beam bombardment.

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Figure 3. Cross-sectional view of the 3D reconstruction for the bulk half-bead on substrate, using (a) a method based on surface topography obtained from simulated AFM data, (b) the contour detection of the interface by the contrast intensity threshold method and (c) the dynamic model-based volume correction combining the AFM data and an empirical sputter model.



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Figure 4. Depth profiles of the multilayered half-bead structure on substrate after 3D reconstruction using (a) the contour detection of the interface by the contrast intensity threshold method and (b) the dynamic model-based volume correction combining the AFM data and an empirical sputter model. Depth profiles of the core-shell half-bead structure on substrate after 3D reconstruction using (c) the contour detection of the interface by the contrast intensity threshold method and (d) the dynamic model-based volume correction combining the AFM data and an empirical sputter model. (e) Depth profiles of the bulk half-bead structure with interlayer on substrate after 3D reconstruction using the dynamic model-based volume correction combining the AFM data and an empirical sputter model. Cross-sectional images show the 3D render using the reconstruction method corresponding to the depth profiles.

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Figure 5. ToF-SIMS results of the original data after 3D reconstruction, corresponding to, (a) the 3D image, (b) the crosssectional view and (c) the depth profile of the metallic multilayer sample.


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Figure 6. ToF-SIMS results of the original data after 3D reconstruction, corresponding to, (a) the 3D image, (b) the crosssectional view and (c) the depth profile of the dPS-b-PMMA diblock copolymer film organized in lamellar structure.

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Table of Contents Graphic:

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Improvement of the Correlative AFM and ToF-SIMS Approach Using

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