Multivariate Analysis Combined with Surface Mass Spectrometry (ToF

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Multivariate Analysis Combined with Surface Mass Spectrometry (ToF-SIMS): Enabling Problem Solving and Expanding Application Space in an Industrial Environment Kathryn G. Lloyd* DuPont Corporate Center for Analytical Sciences, Experimental Station Building 323/103A, 200 Powder Mill Road, Wilmington, Delaware 19803, United States ABSTRACT: Surface characterization techniques, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) in particular, provide data that are multivariate in nature. Multivariate data reduction approaches can extract information and/or represent the data to facilitate interpretation of the data and correlation with measured properties. Examples discussed include multivariate curve resolution analysis of secondary ion mapping data (additive segregation in polymer resin pellets), combined positive and negative secondary ion mapping data after image registration, and secondary ion depth profiling data with depth scale correction.

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INTRODUCTION Surface characterization techniques such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) and ESCA (electron spectroscopy for chemical analysis, a.k.a. X-ray photoelectron spectroscopy) provide chemical information specific to the outermost surface. This can be both an advantage and a disadvantage in industrial problem solving, as even though the cause of the problem may be surface-related the cleanliness of manufactured and/or field-tested surfaces is often uncontrolled. Data from these techniques are always a convolution of signal/response from all species coexisting on the surface, often making the correlation of data to property measurements difficult. SIMS presents a number of additional wrinkles. First, secondary ion yields are a function of both desorption and ionization, resulting in a dependence on chemical environment (so-called “matrix effects”). Thus, Si+ is expected to exhibit a different secondary ion yield depending on whether it originates from a silicon wafer or from silicone oil.1 Second, since SIMS is based on mass detection, spectral features are often subject to mass interferences/overlaps from isobaric species, including species with multiple isotopes. Third, and similar to IR, species manifest in the spectral data as patterns of peaks, not as single features. For many organic/polymeric moieties, these patterns are only distinguishable by relative intensities of low-mass secondary ions. The advent of time-of-flight detection in the early 1990s and the introduction of polyatomic liquid-metal primary ion sources (Au3+, Bi3+) in the 2000s helped to address these problems by providing mass resolution significantly higher compared to quadrupoles and by increasing the ion yields of higher-mass molecularly specific secondary ions, respectively. However, © XXXX American Chemical Society

mass and pattern overlaps are still challenges for polymers and inorganic compounds. A problem inherent to the use of a pulsed primary ion beam in time-of-flight experiments is that there is a trade-off between the best achievable mass resolution and lateral resolution.2 Figure 1 shows the typical mass resolution for data acquired in “spectroscopy” mode compared to data acquired in high-lateralresolution mode. Hence, isobaric overlaps again become problems in mapping mode. All mapping data discussed in this paper were acquired with essentially unit mass resolution. Finally, the sheer volume of data that can now be collected routinely with each mapping or depth profiling experiment can be overwhelming. An entire positive or negative secondary ion spectrum can be collected in each pixel of a pixel array spanning an area of interest, and in addition, this is a function of sputter time for a depth profile. This provides the opportunity to discover unanticipated information in the data but also requires an efficient way of doing so. Although secondary ion mapping data can be acquired in a fraction of the time (usually minutes) required to obtain similar mapping data using electron microscopy/energy-dispersive spectroscopy (EDS), ESCA, or Raman, it can demand significant interpretation time. For all of these reasons, multivariate analysis is becoming a valuable data reduction tool within the ToF-SIMS community, enabling applications in areas spanning electronics to biology.3 This paper discusses examples in which the use of multivariate Special Issue: John C. Hemminger Festschrift Received: May 28, 2014 Revised: July 15, 2014

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7.0.3 (eigenvector Research, Inc., WA), in conjunction with MatLab version R2012b (Mathworks, Inc., MA). Image registration was accomplished using routines from the Matlab Image Processing Toolbox (Mathworks, Inc., MA). The intensities per individual pixels used for the application of MCR to mapping data were not dead-time-corrected. Although intensities per pixel are in general low for these data sets, and the MCR models have not yielded artifacts that suggest dead-time effects, some influence from these effects cannot be ruled out.4 Depth profiling data used for Example 3 and the spatially binned depth profiling data used for Example 4 were dead-time-corrected using the method discussed by Stephan.5

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EXAMPLE 1: SECONDARY ION MAPPING OF ADDITIVE SEGREGATION IN POLYMER RESIN PELLETS The application of multivariate methods to ToF-SIMS mapping data was originally driven by the need to achieve chemical contrast in the face of low overall signal within the static SIMS limit and low/no signal from high-mass molecular ions.6 Although the introduction of Au and Bi liquid metal ion sources (LMIS) has made molecular mapping possible, these methods are still useful in enhancing contrast and extracting characteristic spectral patterns from the data. This example concerns polymer resin pellets that exhibited significant additive phase segregation, leading to manufacturing problems. Figure 2 shows the total negative secondary ion yield map, along with pixel intensity maps of some molecular ions detected from a 100 × 100 μm2 area of a razor-cross-sectioned pellet surface, using a 128 × 128 pixel array. Using the data reduction software, one can extract spectral data from features in the ion maps using intensity thresholding. However, it is rarely possible to completely separate one phase from another spectroscopically using this approach. Note also that the summed intensities of the hydrocarbon-rich phase (green) are weak. The original [128 × 128 pixel array] × [1200 unit-massbinned channels] was unfolded and scaled as recommended by Keenan et al. (“optimal scaling”).7 Multivariate curve resolution (MCR) was used to construct a model consisting of N factors/ “analytes”, with N associated “concentrations” and N associated “spectra”. Descriptions of MCR can be found in the literature.8,9 Principal components analysis (PCA) and a variant known as maximum autocorrelation factor (MAF) have also been used to establish chemical contrast.10,11 In the case of PCA, when there are more than two distinct components, the

Figure 1. (a) Typical mass resolution in mapping mode, compared to (b) typical mass resolution in spectroscopy mode.

analysis has enhanced the ability of ToF-SIMS to provide useful chemical information in an industrial environment.

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EXPERIMENTAL SECTION Secondary ion spectra, maps, and depth profiles were acquired using an ion-ToF ToF-SIMS 4 instrument (ION-TOF, Muenster Germany) equipped with 25 keV gold or bismuth liquid metal primary ion sources. Unless otherwise stated, a pulsed electron flood gun was used for charge compensation. All data used for the multivariate analysis were dead-timecorrected. The standard SiO2/Si wafer (187 nm thick SiO2 layer) was purchased from Geller Microanalytical Lab, Inc. (MA). Multivariate calculations, specifically multivariate curve resolution, were carried out using the PLS_Toolbox, version

Figure 2. Selected negative secondary ion maps acquired over a 100 × 100 μm2 area of a thick razor-cut polymer resin pellet cross-section. B

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Figure 3. (a) Results (“concentrations”/pixel at left and “spectra” at right) from MCR analysis of negative secondary ion mapping data acquired from a 100 × 100 μm2 area of pellet cross-section. (b) Comparison of summed-peak overlay (left) with MCR factor overlay (right). No image adjustments were applied.

Similarly, many species yield more diagnostic features in the negative secondary ion spectra (e.g., acrylics), whereas some species are only observed in positive secondary ion spectra (e.g., alkyl amine/ammonium). Because these two data sets provide complementary chemical information, it has sometimes proven useful to carry out multivariate analysis on the combined data set. The example discussed here is an adhered particle defect remaining on a wafer surface after chemical−mechanical planarization (CMP12) treatment and subsequent cleaning. Roughly 80% of such wafer defects are agglomerated particles from polishing/planarization slurries. Figure 4 shows the results of MCR analyses of the separated positive and negative secondary ion mapping data sets from a 39 × 39 μm2 area (using a 64 × 64 pixel array) including such a defect. The sample is a copper/carbon-doped silica dielectric patterned silicon wafer. One would expect to see silica spectral character from the defect and perhaps to some extent from the dielectric. However, neither model describes the dielectric separately from the particle. Figure 4 also shows the slight shift in field of view between the two data sets. In the practical application of ToF-SIMS, it is rare that the negative secondary ion maps and the positive secondary ion maps overlap completely, even though they are acquired from the same location. The difference in polarity of

interpretation/assignments of the spectral loadings are not always straightforward. In the case of MAF, the applied scaling results in optimized contrast, but the spectral loadings are not conducive to interpretation. Figure 3 shows the results of MCR analysis of the negative secondary ion mapping data. Note the increase in contrast in the factor overlay compared to the summed-peak overlay in Figure 2. Note also the usefulness of the factor spectra, even as they have been kept in optimal scaling space. Irganox1010 antioxidant was known to be a component in a rubber additive, and a specific alkyl amide was clearly identified as playing a role in the phase segregation. Multivariate analysis remains additive to secondary ion mapping experiments, and the author uses it routinely.

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EXAMPLE 2: CORRELATION OF POSITIVE AND NEGATIVE SECONDARY ION MAPPING DATA USING IMAGE REGISTRATION AND MCR Two sets of surface mass spectral data are usually acquired as part of any ToF-SIMS mapping analysis: the positively charged and negatively charged secondary ion data. It is perhaps obvious that many elements are more easily detected as the positively charged secondary ion (e.g., Na, Cu), whereas other elements are more easily detected as the negative-charged secondary ion (e.g., F, Au), based largely on electronegativity. C

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Figure 4. Results from MCR analysis of separate positive and negative secondary ion mapping data.

Figure 5. Results from MCR analysis of the concatenated [positive + negative] secondary ion mapping data set.

hyperspectral stack of the nonreference data set. Rows or columns that do not overlap are clipped. For the case discussed here, the two spatially registered data sets (positive and negative secondary ion mapping data) were optimal-scaled separately and then concatenated for each pixel. Optimal-scaled positive secondary ion intensities from unit m/z 10−260 were followed by optimal-scaled negative secondary ion intensities from unit m/z 18−270, yielding a total of 504 mass variables. Figure 5 shows the results of MCR analysis of this combined data set. Factor “spectra” have been displayed by plotting values from mass variables 1−251 vs the positive ion m/z scale 10− 260 and plotting values from mass variables 252−504 vs the negative ion m/z scale 18−270. The carbon-doped silica component of the dielectric is clearly differentiated from the silica defect. (Undoped) silica is detected to some small extent from the dielectric. Thus, there is not just the advantage of correlating complementary data but also the potential for synergy in combining the positive and negative secondary ion mapping

the analyzer extraction voltage, the different behavior of the ions and emitted electrons in response to the chargeneutralizing flood gun, and the charging behavior of the sample all influence the direction of the incoming primary ion beam. These offsets are largely compensated for in tuning, but often not completely. Hence, the two sets of mapping data must be registered spatially before being combined. At the level of the routines available in the Matlab Imaging Toolbox, the user must first choose a set of points that mark features common to the two sets. The total ion yield maps can be used for this, or to enhance contrast, a preliminary MCR or PCA analysis can be carried out on each data set. These points are used to compute a transform function, the form of which is chosen by the user (the form of transform function used for this example was “nonreflective similarity”). One of the data sets is chosen as reference, and the transform function is applied to the other data set. Overlays and simple matrix correlation functions can be used to determine if the transform is satisfactory. The transform is applied to each mass channel map in the D

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Figure 6. (a) Selected positively charged ion intensities as a function of sputter time; (b) MCR analysis of the depth profiling data (factor representing outer surface adventitious contamination not shown); (c) MCR factor representations.

data. This synergy may not be as important for high-massresolution spectral data. However, mapping data from this field of view cannot be acquired with high mass resolution. This protocol also sets the stage for correlation of multitechnique mapping data, which is the subject for another article.

Figure 7. Effect of roughness on 3D representation of as-collected depth profiling data.

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EXAMPLE 3: MULTIVARIATE ANALYSIS APPLIED TO TOF-SIMS DEPTH PROFILING DATA In a ToF-SIMS “dual-beam” depth profiling experiment, a separate sputter beam is used to remove layers from the surface, creating a crater. At regular intervals, or as part of the acquisition timing cycle, the sputter beam is stopped, and the primary ion beam (Bi1+, Bi3+) is used to generate positive or negative secondary ion spectra from a smaller area within the crater. Traditionally, SIMS and ToF-SIMS profiles have been carried out using an oxygen sputter source to enhance positive secondary ion yields or a cesium source to enhance negative secondary ion yields.13 Although molecular secondary ions characteristic of organic material are destroyed using these sputter beams, high-mass patterns exist for inorganic materials. For qualitative analyses, multivariate analysis provides an alternative to resolving mass interferences and monitoring single ion peaks as a function of depth. As an example, Figure 6 compares the results of univariate depth profile analysis of an ca. 200 Å-thick SiO2 layer on a silicon standard sample with results from an MCR analysis of the same data set. A 1 keV oxygen beam was used to sputter a 500 × 500 μm2 crater. Positive secondary ion spectra were acquired after 5 s sputter intervals using a Bi1 primary ion beam from a 100 × 100 μm2 area centered within the crater. The optimal-scaled data set (one total-area spectrum per depth slice) was modeled using MCR. A three-factor model describes >96% variance in the data. This analysis provides the means to monitor each silicon-containing species (as sputtered by an oxygen beam). Of course, the thickness of the oxide layer could be more rigorously determined via a negative secondary ion profile using a cesium sputter source. However, oxygen sputtering combined with MCR analysis enables the discovery of other elements and impurities in real systems.

It has been demonstrated that profilometry measurements obtained from the same area prior to depth profiling can be used to adjust the z scale based on initial topography.14 Alternatively, if the depth profile reaches a known or nominally flat layer/substrate and lateral resolution better than the roughness is retained, the point at which this layer is reached can be used to adjust the depth scale on a pixel-by-pixel basis.15 Applying the correction only to selected peaks or sums of peaks does not take advantage of all the information contained in the data. The entire profile data set can be saved depth slice by depth slice and reconstructed in Matlab for a comprehensive multivariate analysis, but in most cases this approach is limited by computer memory. The PCA approach used by Fletcher et al.16 to correct the depth scale for biological cells shows how distinct chemical phases can be visualized in three dimensions. An approach which the author has found useful is to first extract profile data (spectra with 500 or more unit-resolution mass channels) from a set of spatially binned pixel blocks spanning the profiled area. MCR analyses are then carried out on each of these subprofiles, using factor spectral constraints from an MCR model used to describe secondary ion mapping data acquired from the sidewall (see Figure 8). This establishes the same factor ordering from each subprofile model, while allowing concentration levels and appearance time to vary for each group. The factor representing the known flat layer/ substrate is used to determine shifts in the other factors. This approach is inherently “blind” to chemistry outside of the sidewall model (e.g., buried particulate, which should be at least hinted at from a first exploratory look at the data), and the depth correction is only as good as the binned lateral resolution. However, most of the steps can be automated, and thus it serves as a “quick-and-dirty” visualization method that makes use of the chemical information multivariate analysis provides. The example shown is from depth profiling analysis of a CZTS (Cu−Zn−Sn−S) precursor ink printed on a molybdenum-coated glass substrate. The sample was prepared as described in ref 17. A 1 keV oxygen beam was used to sputter a 250 × 250 μm2 crater. Positive secondary ion spectra data were acquired using a Bi1 primary ion beam after each 30 s sputter interval from a 64 × 64 pixel array spanning an 82 × 82 μm2

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EXAMPLE 4: USING MULTIVARIATE ANALYSIS TO CORRECT FOR APPARENT Z-INVERSION One problem frequently encountered when depth profiling nonstandard samples is inversion of the depth scale resulting from surface roughness that translates through the entire profile, as illustrated in Figure 7. E

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surface MCR factor to more accurately represent the roughness that is present.

Figure 10. 3D maps of (a) an uncorrected surface Na−NaOH− NaCO3-rich factor from MCR analysis and (b) the same MCR factor after z-correction.

As organic/molecular depth profiling becomes a reality through the use of large argon cluster sputter beam sources,18 applications to biological systems will continue to increase. Depth profiling data from these systems will require depth scale correction to properly visualize and associate chemistry with biological features. Multivariate methods will be vital to understanding the spectral data and chemistry from these systems.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

Figure 8. (a) Video capture of a nominally 250 × 250 μm2 sputter crater at the end of the CZTS depth profile. (b) Total positive secondary ion yield map from the 150 × 150 μm2 area of the crater sidewall. (c) MCR factor overlay representation of the sidewall chemistry. (d) MCR factor spectra.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The author acknowledges James Marsh for collecting much of the ToF-SIMS data discussed in this paper.

area. The total depth is estimated to be 1.0−1.5 μm. The MCR factor overlay for the sidewall map is shown in Figure 8. MCR analysis reveals a surface layer rich in sodium and sodium carbonate: two CZTS factors, one of which is higher in Na and K (likely migrating from the glass through the Mo), and molybdenum. Figure 9 compares the 3D representation of the “raw” Mo intensity per voxel with the 3D representation of the MCR

REFERENCES

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Figure 9. 3D maps of (a) “raw” Mo signal (sum of 92,95,96,98 Mo); (b) uncorrected spatially binned Mo-rich factor from MCR analysis; and (c) z-adjusted spatially binned Mo-rich factor from MCR analysis.

factor characteristic of the Mo layer, as well as with the zcorrected 3D representation of this same factor. Note that the Matlab “Jet” colormap represents low-intensity/“concentration” as blue and high intensity/“concentration” as red. Shifts were determined by first filtering (using a running average with window size = 5) each Mo factor profile and then choosing the layer start time using a threshold value for rate of rise. Figure 10 shows how the z correction changes the 3D appearance of the F

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