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Research ArticleReceived: 14 August 2007 Revised: 29 November 2007 Accepted: 1 December 2007 Published online in Wiley Interscience: 20 February 2008

(www.interscience.com) DOI 10.1002/sia.2731

XPS spectromicroscopy: exploiting therelationship between images and spectraJohn Waltona∗ and Neal Fairleyb

The application of spectroscopic processing techniques to multi-spectral XPS data sets has enabled the acquisition ofquantitative surface chemical state images. Such data sets are necessarily large, incorporating many spectra, so prohibitinginteractive processing. Instead multivariate analytical techniques are used to reduce the dimensionality of the data, andalso to increase the signal/noise, there bye speeding acquisition. These techniques may also be used to classify regions inimages according to different chemistry, that is changes in photoelectron intensity, changes in binding energy and changesin the inelastic background. Spectra from classified regions may then be summed to aid visualisation, obviating the needfor multivariate curve resolution with its attendant uncertainties. Further the inelastic background of transmission correctedspectra from classified regions may be modelled to provide spatially resolved in-depth information. Such classification also aidscurve fitting, since curve fit models can be applied to regions of similar chemistry. Copyright c© 2008 John Wiley & Sons, Ltd.

Keywords: XPS; spectromicroscopy; quantitative; images; NIPALS; MCR

Introduction

X-ray photoelectron spectroscopy is a mature surface analyticaltechnique, with a large installed instrument base, providingrelatively easily quantified chemical state information, which isreflected by the number of publications in scientific journals.However, despite the fact that the first commercially available XPSinstrument capable of imaging was announced in 1990,[1] relativelyfew publications, by comparison, have concerned imaging XPS.This is a consequence of instrument manufacturers implementingthe acquisition of single energy images as a guide to selected areaanalysis, and as a result, losing the very features that made XPSspectroscopy popular, namely, quantification and the acquisitionof chemical state information. Single energy images, and evenpeak minus background images, are incapable of accounting forthe background below photoelectron peaks, or for changes in thepeak shape due to chemical shifts. Further, they cannot resolveoverlapping photoelectron peaks. Indeed, even as a guide toselected area analysis, single energy images perform poorly, sincethey can only truly represent changes occurring across an image ifthere are only two components which change. Yet the acquisitionof quantifiable chemical state images is clearly preferable toanalyses at a number of discrete points.

More recently, however, a number of publications havedescribed the acquisition of spectrum image datasets,[2] whereeach pixel in an image contains a spectrum. Such datasetsmay be acquired by scanning an X-ray probe over the surfacewhile acquiring a spectrum at each pixel, or by acquiring aseries of energy-filtered images incremented in energy, knownas parallel imaging. The latter mode is preferred since it isquicker and minimises sample damage due to X-ray exposure. Twodifferent types of instruments are capable of parallel acquisition.One employs a cathode lens,[3] the other a combinationmagnetic/electrostatic lens. Using laboratory sources, the formerhas better spatial resolution, approximately 0.7 µm, but cannotanalyse insulators due to the difficulty of charge neutralisationin the high electric field between the sample and the cathode

lens. The latter type of instruments have a resolution of about3 µm; one utilising a Fourier transform lens and hemisphericalenergy analyser where images are acquired at a fixed retardratio,[4] and another using a spherical mirror energy analyser,where images are acquired at constant analyser transmission,[5]

and where charge neutralisation is achieved by using a supply oflow-energy electrons constrained by the magnetic field.

The spectrum image data-sets acquired in this manner arenecessarily large, and the use of multivariate analytical techniquesis necessary to both reduce the dimensionality of the data-sets, thereby simplifying data processing, and also to improvethe signal/noise, so reducing the time required for acquisition.The authors have reported characterisation of the performanceof a parallel imaging instrument[6] and made recommendationsconcerning modes of operation, and procedures to correct for theintensity/energy response function.[7] Curve fitting to spectrumimage data-sets to obtain chemical state information has alsobeen reported.[8,9] It is, therefore, possible to apply spectroscopicdata-processing techniques to spectrum image data-sets and toobtain spatially resolved, quantified, chemical state information.However, these procedures only involve treating the data-setas a stack of spectra. Here, we show for the first time, that bymaintaining the relationship between images and spectra, it ispossible to progress beyond the application of spectroscopicprocessing to spectrum image data-sets, by utilising the three-dimensional information contained in such data-sets, to thereforeimprove both the processing and the visualisation of the data.

∗ Correspondence to: John Walton, School of Materials, The University ofManchester, Manchester, UK. E-mail: john.walton@manchester.ac.uk

a School of Materials, The University of Manchester, Manchester, UK

b Casa Software Ltd, Teignmouth, UK

Surf. Interface Anal. 2008; 40: 478–481 Copyright c© 2008 John Wiley & Sons, Ltd.

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XPS spectromicroscopy: utilising images and spectra

Background

The acquisition of XPS multi-spectral data-sets and their subse-quent analysis requires the use of multivariate analytical tech-niques to reduce their dimensionality, since there are too manyspectra to interrogate individually. One such technique is principalcomponent analysis (PCA),[10] which assumes that any data-set canbe described as a linear combination of components, which are or-dered by variance. Because these components are orthogonal, thatis, no one component is contained in another, this is the minimumnumber of components needed to represent the data. Further,reconstructing the data using only those components containingphysical information, and omitting those containing noise, resultsin a significant improvement in the signal/noise. One of the mostpopular solutions of PCA is the singular value decomposition ofthe covariance matrix, formed by multiplying the data matrix by itstranspose. However, this procedure is computationally intensive,since it scales as the cube of the size of the data. Splitting the datainto subsets, and then performing PCA on a final matrix consistingof the top few abstract factors of each subset, results in a significantreduction in computational requirement, but at the expense ofthe number of abstract factors. Similarly, prior ordering the databy variance and then performing PCA on a reduced matrix hasalso been shown to reduce computational requirements,[11] butis unable to adequately extract the higher order abstract factorsfrom the noise. An alternative procedure, proposed by Wold,[12]

is non-linear iterative partial least squares (NIPALS).[13] This en-tails sequentially calculating the individual Eigen vectors of thedata matrix. The procedure can then be terminated when theappropriate number of abstract factors has been determined. Thisprocedure is ideally suited to XPS, where typically, there are manyfewer abstract factors containing physical information than thereare objects in the data-set. In practice, more abstract factors arecalculated so as to determine how many are needed to accuratelyreconstruct the original data.

However, the computed abstract factors bear no resemblanceto physical data, and so procedures have been designed to rotatethe vectors in order to obtain correspondence with physical data,known as multivariate curve resolution (MCR). This usually entailsconstraining the abstract factors so that they do not containnegative values. The procedure was originally developed forchromatography, where negative solute concentrations wouldbe meaningless, but it is not clear that this is a sufficient criterionfor XPS data, where changes in the inelastic electron backgroundacross the field of view would necessarily be reflected in the higherorder abstract factors, so allowing inverse peaks, or troughs withnon-negative values.

An alternate method to visualise spectra from the differentcomponents is to utilise the relationship between images andspectra, so that real spectra from different regions defined in animage may be displayed. Any image may be used, e.g. atomicconcentration images, or image abstract factors. Classifying thepixels in an image abstract factor by intensity orders the dataaccording to changes in chemistry. This is because the data matrixis decomposed into two matrices, one composed of intensities andthe other composed of components, often referred to as scoresand loadings, respectively. So for example, if an image data-setconsisting of X images can be decomposed into Y components, theintensity matrix will consist of X by Y vectors, and the componentsmatrix Y by 1 vector, or Y image abstract factors. Classification ofthe pixels in an image abstract factor by intensity therefore, ordersthe data due to changes in the spectra associated with those

pixels; that is, changes in photoelectron peak intensity, changes inphotoelectron peak position due to chemical shifts, and changesin the inelastic background. The unprocessed spectra within eachimage classification may then be summed. The same proceduremay also be used to improve curve fitting by deriving curve fitmodels for spectra with similar chemistry, and then applying thosemodels only within their own classification. This procedure haspreviously been reported.[14]

The procedure is demonstrated using an example from afailed metallisation layer consisting of silver deposited onto nickeloverlying a titanium layer, in order to visualise spectra from thedifferent components and to determinate the oxide film thicknessfrom a selected region of the image.

Experimental

XPS analysis was undertaken using a Kratos Axis Ultra instrumentlocated in the School of Materials at The University of Manchester.The instrument utilises a magnetic immersion/electrostatic lens,spherical mirror analyser for imaging, and channel plate/delay linedetector for pulse-counting electrons. The spatial resolution ofthe instrument is 3 µm at 200 by 200 µm field of view, and scaleslinearly with the field of view.

A sample from a failed metallisation layer, consisting of silverdeposited onto nickel overlying a titanium layer was attachedto a sample bar using double-sided adhesive tape so that itwas electrically isolated, thereby minimising vertical differentialcharging. Charge buildup at the sample surface due to emissionof electrons was compensated for by using a low-energy electronflood source, operating at 1.8 A filament current, 3 V chargebalance and 1.2 V filament bias. A spectrum image data-set wasacquired from a 800 by 800 µm area, using a monochromatic Al Kα

X-ray source operating at 150 watts, irradiating a 3 × 1 mm area.The photon energy of the source was determined as 1486.6 eV,by comparing the positions of the Cu (LMM) and Ag (MNN) Augerpeaks, and the Ag 3d5/2, Cu 2p3/2 and Au 4f7/2 photoelectronpeaks used for energy calibration, with reference values. Thespectrum image data-set consisting of 901 images, of 256 by 256pixels, was acquired from 900 to 0 eV binding energy, in 1 eVsteps, at 160 eV pass energy, equivalent to a resolution of 3.6 eVfull width half maximum (FWHM), and with 5 s dwell time perimage. All data processing was carried out using CasaXPS.[15] Thedata-set was reduced to 128 by 128 pixels to aid processing andcorrected for the intensity/energy response of the instrument,which had been previously determined.[16] The NIPALS procedurewas used to compute the image abstract factors, following Poissonpre-filtering.[11] The signal/noise in the data-sets was improved byreconstructing the data-sets using only the first six abstract factors.The images were then converted to spectra, and quantified bymeasuring photoelectron peak areas using a Shirley background,and theoretical sensitivity factors, at every pixel in the image, toproduce atomic concentration images.

In order to visualise the spectra associated with differentcomponents in the image, the pixels in the second imageabstract factor were classified by intensity, and the raw spectrawithin each classification summed. In this way, spectra fromimage regions where PCA-indicated different chemistry couldbe displayed, without application of the PCA procedure to thespectra. Following quantification, pixels with high intensity inthe Ni 2p atomic concentration image were classified and thespectra summed to allow modelling of the inelastic background

Surf. Interface Anal. 2008; 40: 478–481 Copyright c© 2008 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/sia

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J. Walton and N. Fairley

from the O 1s photoelectron region using the QUASES softwarepackage. This procedure utilises a ‘Tougaard’ description of theinelastic background, so that correction for the intensity/energyresponse of the instrument is a prerequisite. Modelling the inelasticbackground to the mean spectrum allows the procedure to beundertaken interactively and without any assumptions as to thein-depth distribution. The analysis may be applied to the wide-scan spectrum acquired for quantification so that no further dataneeds to be acquired.

Results and Discussion

Figure 1 shows the first nine image abstract factors from thedata acquired on the failed metallisation layer. The first imageabstract factor contains the largest variance in the data andshows features due to the inhomogeneity of the X-ray fluxand striations arising from the delay line detector. Subsequentabstract factors show decreasing variance, and the first six wereused to reconstruct the data, before converting the images tospectra. Figure 2 shows atomic concentration images obtained byquantifying the spectrum at each pixel, and which show eithersimilar or complementary distributions across the image as wasseen in the second abstract factor. The failed layer shows highconcentrations of nickel and oxygen, in contrast to the adherentregion where silver predominates. Carbon is distributed across thesurface, but shows higher concentrations on the silver, which hadbeen exposed to ambient for longer. The images suggest failure ofthe metallisation layer was due to oxidation of the nickel surface.Figure 3 shows the false colour image, inset, following classificationof the pixels in the second image abstract factor by intensity. Thespectra within each classification have been summed, and are

Figure 2. Atomic concentration images from the failed metallisation layer(800 by 800 µm field of view). This figure is available in colour online atwww.interscience.wiley.com/journal/sia.

coloured according to their classification. Visualisation in thismanner, allows the spectral shape to be examined, where thehigh background of the Ag 3d photoelectron peak can be seento be associated with an increase in C 1s intensity from theoverlayer contamination. Since the data has been corrected forthe intensity/energy response of the instrument, it is possible to

Figure 1. First nine image abstract factors from the failed metallisation layer (800 by 800 µm field of view). This figure is available in colour online atwww.interscience.wiley.com/journal/sia.

www.interscience.wiley.com/journal/sia Copyright c© 2008 John Wiley & Sons, Ltd. Surf. Interface Anal. 2008; 40: 478–481

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XPS spectromicroscopy: utilising images and spectra

Figure 3. Spectra summed from regions indicated in the false colour imageproduced by pixel classification of the second image abstract factor (inset,800 by 800 µm field of view). Upper region in the classified image is nickelrich, and the lower region silver rich. This figure is available in colour onlineat www.interscience.wiley.com/journal/sia.

model the inelastic background to obtain in-depth information. Anumber of methods to obtain such information from imaging XPShave already been reported in the literature. Walton and Fairley[11]

produced images of relative oxide thickness by dividing the area ofthe oxygen 1s photoelectron peak by the height of the loss featureat higher binding energy. The procedure is simple to implementand provides an image of the relative film thickness, where brightregions represent thinner overlayer films. Smith et al.[17] wereable to measure the oxide thickness of features on a germaniumsurface by incorporating the Hill equation[18] into their analysis.By considering the ratio of the chemically shifted oxide arising inan overlayer to the metal photoelectron peaks from the substrate,they were able to calculate a map of oxide thickness. More recently,Hajati et al.[19] have produced images of the ‘amount of substance’at different depths by using both photoelectron peak areas andmodelling of the inelastically scattered background. They assumedan exponential depth dependency, so that intervention by theanalyst was not required.

To obtain in-depth information from the failed metallisationlayer, the pixels in the nickel atomic concentration imagehave been classified by intensity, so that only regions rich innickel are included in the summed spectra. Figure 4 shows theclassified image and the mean spectrum around the oxygen1s photoelectron region. The inelastic background has beenmodelled using the Quases[20] software package using the TPP-2Mmethod to calculate the inelastic mean free path, and indicatesan oxide thickness of 7.2 nm. Since this is much greater thanthat expected for an air-formed oxide, approximately 1 nm, itis further proof that failure occurred in the oxide layer. In thisexample, no restrictions need to be applied concerning the in-depth distribution, and the procedure can take place interactively,providing an indication of the mean film thickness. A more accurateindication of the film thickness may be achieved by first modellingthe inelastic background from a bulk oxide, checking to ensurethat the calculated depth corresponds to sufficient multiples ofthe inelastic mean free path, before applying the result to the datafrom the thinner layer.

Figure 4. Modelling of the inelastic background of the O 1s photoelectronpeak in the spectrum obtained by summing spectra from the regionindicated in the Ni 2p atomic concentration image following pixelclassification (800 by 800 µm filed of view).

Conclusion

The task of analysing multi-spectral data-sets which may consistof 65 536 spectra is not trivial. Multivariate analysis has beensuccessfully used to reduce the dimensionality of the data, andspectroscopic processing techniques applied to the spectra ateach pixel. Maintaining the relationship between the imagesand the spectra has aided visualisation of spectra from differentcomponents within an image, without recourse to MCR withits attendant uncertainty. Further, it has aided modelling of theinelastic background in unprocessed spectra from selected areasof the image, to extract in-depth information.

References

[1] Coxon P, Krizek J, Humpherson M, Wardell IRM. J. Electron. Spectrosc.Relat. Phenom. 1990; 52: 821.

[2] Ferryman AC, Fulghum JE, Gianuzzi LA, Stevie FA. Surf. InterfaceAnal. 2002; 33: 907, DOI:10.1002/sia.1448.

[3] Omicron Nanotechnology GmbH. www.omicron-instruments.com.

[4] Thermo Fisher Scientific. www.thermo.com.[5] Kratos Analytical. www.kratos.com.[6] Walton J, Fairley N. Surf. Interface Anal. 2006; 38: 1230,

DOI:10.1002/sia.2381.[7] Walton J, Fairley N. Surf. Interface Anal. 2006; 38: 388,

DOI:10.1002/sia.2131.[8] Walton J, Fairley N. Surf. Interface Anal. 2004; 36: 89,

DOI:10./1002/sia.1654.[9] Walton J. Surf. Interface Anal. 2007; 39: 337, DOI:10.1002/sia.2510.

[10] Malinowski ER. Factor Analysis in Chemistry (3rd edn). John Wiley:New York, 2002.

[11] Walton J, Fairley N. J. Electron Spectrosc. Relat. Phenom. 2005; 148:29, DOI:10.1016/j.elspec.2005.02.003.

[12] Wold H. Eur. Econ. Rev. 1974; 5: 67, DOI:10.1016/0014-2921(74)90008-7.

[13] Geladi P. J. Chemom. 1988; 2: 231, DOI:10.1002/cem.1180020403.[14] Walton J. Surf. Interface Anal. 2007; 39: 337, DOI:10.1002/sia.2510.[15] http://www.casaxps.com.[16] Walton J, Fairley N. Surf. Interface Anal. 2006; 38: 388,

DOI:10.1002/sia.2131.[17] Smith EF, Briggs D, Fairley N. Surf. Interface Anal. 2006; 38: 69,

DOI:10.1002/sia.2199.[18] Hill JM, Royce DG, Fadley CS, Wagner LF. Chem. Phys. Lett. 1976; 44:

225.[19] Hajati S, Coultas S, Blomfield C, Tougaard S. Surf. Sci. 2006; 600:

3015, DOI:10.1016/j.susc.2006.05.020.[20] Quases Tougaard ApS. www.quases.com.

Surf. Interface Anal. 2008; 40: 478–481 Copyright c© 2008 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/sia