MATERIALS FORUM VOLUME 30 - 2006Edited by R. Wuhrer and M. Cortie© Institute of Materials Engineering Australasia Ltd

CHARACTERISATION OF MATERIALS THROUGH X-RAY MAPPING

R. Wuhrer\ K. Moran'r' and L. Morarr'

1 Microstructural Analysis Unit, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia.E-mail: Richard.Wuhrer@uts.edu.au

2 Moran Scientific Pty Ltd, 4850 Oallen Ford Road, Bungonia, NSW, 2580, Australia.E-mail: kmoran@goulburn.net.au

ABSTRACT

Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), wavelength dispersive spectroscopy(WDS) and the combination of these techniques through x-ray mapping (XRM) have become excellent tool forcharacterising the distribution of elements and phases in materials. Quantitative x-ray mapping (QXRM) enablesreliable quantitative results that can be an order of magnitude better than traditional analysis and is also far superior toregions of interest x-ray maps (ROIM) where low levels of an element or elemental overlaps are present.

With the development of faster computers, improved electronics, multi-detector systems and more powerful software,XRM can be performed with an EDS spectrum and/or WDS data collected at each pixel point of the SEM image. Thisallows full characterisation of a material at a latter stage off-line from the instrument. To obtain a better understandingof a material's chemical and microstructural properties a number of post-processing methods, such as elementalmapping, pseudo colouring, ratio mapping, scatter diagram creation, rotational scatter diagrams and phase mappingshould be employed.

1. INTRODUCTION

Keywords: X-ray mapping, pseudo colouring, quantitative analysis

Characterisation is an essential aspect of materialsresearch and of quality control in materials production.Characterisation of materials frequently involves thedetermination of point-to-point variation incomposition, structure and microstructure, so that avariety of imaging and analysis techniques come intoplay. Understanding the distribution of elements andphases in structures is critical to optimising theperformance of all materials. Subtle changes incomposition have been studied for many years todetermine the properties of many materials and aid inthe production of these materials.

Scanning electron microscopy (SEM) micrographsprepared with the back scattered electron signal (BSE)can provide direct information on compositionalheterogeneity through the mechanism of atomicnumber contrast. BSE images are quite useful forcharacterising microstructures, but the compositionalinformation that they contain is non-specific. The BSEimage contains no information to identify the elementspresent or the concentration levels. A BSE image withenergy dispersive X-ray spectrometry (EDS) analysis isan established technique for the analysis of thechemical composition of materials in a SEM.

More recently, x-ray mapping (XRM) is becomingimportant in understanding the elemental distributionsin materials. XRM is the collection of characteristic x-rays as a function of the position of the scanningelectron beam on the specimen. This analyticaltechnique provides a high magnification image relatedto the distribution and relative abundance of elements

within a given specimen. This capability makes XRMparticularly useful for: (i) identifying the location ofindividual elements and (ii) mapping the spatialdistribution of specific elements and phases within asample (material surface).

Currently, x-ray mapping is an extremely usefulproblem solving tool. However, the two majorproblems for energy dispersive spectroscopy areinterpretation of results under non ideal conditions(strong overlap and small peak size relative tobackground), and the time required to obtain a goodquality map (4 to 12 hours). Most mapping is relegatedto out-of-hours (overnight) mapping. With thedevelopment of high count rate detectors and multi-detector systems, the time required to acquire XRMdecreases. With a single EDS detector at 20kcpsoutput, a reasonably good 256x256 quantitative mapcan be obtained in around I hour for major elements(> IOwt% evenly distributed) and minor elements(>Iwt% localised). It is now possible to quantitativelymap using multi-detectors and combined detectors suchas WDS-EDS, hence not only reducing mapping timebut also improving the ability to map trace elementsvery accurately [1].

If utmost care is taken with collecting and processingx-rays maps, then further processing such as scatterdiagram creation, chemical imaging, ratio mapping andphase mapping can be performed. In this paper we willbe discussing region of interest mapping (ROIM),quantitative x-ray mapping (QXRM), full-spectrum x-ray mapping, and the use of scatter plot diagrams forexamining compositional maps and reconstructing the

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spatial distribution of a phase in a chemical image ofthe specimen.

2. TYPES OF X-RAY MAPPING (XRM)

There are many types of x-ray maps, which include 1)dot mapping, 2) regions of interest (ROI) mapping, 3)background subtracted and overlap corrected mapping,4) quantitative mapping and 5) full spectrum mapping(or spectrum mapping). An understanding of thesedifferent types of x-ray mappings as well as subsequentlimitations, are required before correct interpretation ofthe results can be made.

2.1 X-ray Dot Mapping

X-ray dot mapping is two dimensional scanning, whichinvolves taking the output of the single channelanalyser (SCA) and using it to modulate the brightnessof the CRT during normal electron raster scanning.Each x-ray photon detected appears as a dot on theCRT, with regions of high concentration characterisedby a high dot density [1, 2]. The limitations with thistechnique is that it is qualitative only and gives noquantitative information, lacks background correction,is slow and gives poor contrast sensitivity at highconcentration levels. Today, this type of mapping israrely performed due to the greater advantages of theother types of x-ray mapping.

2.2 Regions of Interest (ROI) maps

Regions of interest mapping (ROIM), is where you setup a number of channels or an energy range where aelemental line exists to see the distribution of theelement you are interested in. ROIs are assigned to asingle channel analyser (SCA) and a different colour isassigned for different elements. X-ray maps are usuallycollected using raw counts from the elemental peaks ofinterest, however traditional region of interest mapping(ROIM) does not always provide a completely accuratedescription of the material phases or elementaldistribution. The integrated peak counts are onlypartially related to the true composition of the materialand in fact may not be related at all. Indeed, usingROIM may introduce errors and misleading results.

Figure 1 shows a comparison between RIOM andQXRM for a barnacle attached to an aluminium hullplate. Figures la and lb show the intensity x-ray mapfor aluminium and calcium, where the aluminium is onthe left hand side of the image and is represented asregion (i). Region (ii) is the barnacle and region (iii) isthe resin that that has been used to mount and polishthe sample. Often from a visual point of view thechlorine ROI map (Figure lc), looks better than thechlorine QXR map (Figure Id), and is consequentlyused for presentation. The reason is that the intensitymap has more x-ray counts, as the background has notbeen removed, giving improved counting statistics andthus making the image look better (smoother). It isobvious that the ROIM for chlorine (Figure 1c) isincorrect and actually inverted. The ROIM of chlorine(Figure lc), shows the magnesium plus aluminium sumpeak as well as the tail edge of the aluminium sumpeak in region (i), which occurs at the energy forchlorine and region (ii) is brighter than it should bebecause the background has not been subtracted. For atrue spatial elemental distribution we requirebackground subtraction, overlap correction and inter-element correction. Thus the quantitative map (Figureld) shows the correct spatial element distribution. TheROIM is very problematic when looking at moderate tolow levels of elements.

The accuracy of ROIM is dependent on what elementsare present in the material. If the elements are presentin reasonable amounts and have no overlaps with otherelements then the ROIM would be near to correct.However, great care must be taken for other artefacts[3], such as escape peaks and sum peaks that can occuron the EDS spectrum.

2.3 Background subtracted and overlap correctedmaps

As the name implies, these maps involve backgroundsubtraction and overlap removal. This is required toaccurately understand the elemental distributions in thematerial, especially where overlapping peaks exist.Background subtracted and overlap corrected maps aremore accurate than ROI maps. These maps reflect trueelemental distribution, except in cases of severeabsorption.

a. b. c. d.Figure 1: A barnacle attached to an aluminium plate which has been set in a resin, sectioned and polished. a) RIOM foraluminium, b) RlOM for calcium c) RIOM for chlorine and d) QXRM for chlorine. Region (i) is the aluminium platewith <0.02 wt% CI (less than the EDS detection limit), region (ii) is the barnacle with -0.03 wt% CI and region (iii) is

the resin with -1.3 wt% CI (HWOF=260um).

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can be seen, the quantification of the data is critical foraccurate elemental mapping. Figures 2a-c are the ROImaps and show lovely features as well as a smoothlooking maps however, it is not until the maps arequantified (Figures 2d-t) that the true elementaldistribution is revealed. Furthermore, the WDS maps(Figures 2g and 2h) show even further subtle changesthat the EDS detector cannot reveal due to the poorerdetection limits.

2.4 QUANTITATIVE X-RAY MAPPING (QXRM)

Quantitative maps involve converting the backgroundand overlap corrected maps to weight percents by somecorrection technique, such as phi-rho-z (epz) or ZAF(atomic number, absorption and fluorescencecorrection). Figure 2 shows ROIM, QXRM and WDSmaps for a hard facing material bonded to a steelsubstrate. The elements manganese, silicon andmolybdenum were deliberately chosen due to theredifficulty in mapping these elements in this sample. As

a.

b.

c.

ROIMaps

d.

e.

f.

Quantitative Maps

g.

h.

i.

WDS X-ray maps

Figure 2:. Hard :acing material bonded to a steel substrate. The steel substrate is the upper left hand side of image. a-c)EDS region ofmterest x-ray maps for Mn, Si and Mo, d-f) Quantitative x-ray maps for Mn (-lwt%), Si (-lwt%) andMo (-2wt%), g-~) WDS x-ray maps for Mn and Si and i) Pseudo colouring map of three elements (Cr-red, Fe-green

and Ni-blue), Maps collected at 20kV (HWOF=IOOum). Wt% values are average matrix values.

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Traditional QXRM requires that all the elements ofinterest be known before the mapping proceeds so thatthey could be extracted from the full spectrum at eachpixel during mapping. One of the limitations ofquantitative mapping is that there could be otherelements present, that were initially neglected, and thusa complete new map would be required for any newelements to be ascertained. This constriction was dueto the memory needed to retain the full spectrum ateach pixel (approx 500Mb compared with 20Mb). Withpresent computer technology this is no longer alimiting factor.

Because of the high spatial resolution of the maps, wecan very easily choose methods that allow for theaveraging of large numbers of image points. Thisallows high quality analyses to be carried out onspectra that have statistically significant peaks ofelements present in low concentration. Quantitative x-ray mapping enables us to achieve this as well asgiving us a means to verify the results by postprocessing to determine actual analyses of spatiallydistinct areas of the sample from the stored data. Inother words, QXRM enables reliable analyses, whichare extracted from the maps, that can be an order ofmagnitude better than traditional spot or area analyses.

2.5 Full Spectrum maps or Spectrum mapping

Advances in computer processing speeds, and mediacapacity, has made acquisition of full-spectrum x-raymapping (FSXRM), whether using energy dispersivespectroscopy (EDS) or wavelength dispersive

spectroscopy (WDS), a very popular characterisationtool for determining the elemental distribution inmaterials. Full spectrum mapping saves a spectrum foreach pixel of an image. Spectrum mapping wasdesigned as a full data collection package allowing theuser to acquire data and recall all information at a latertime. This form of mapping allows the user, at whim,to create new maps (QXRM or ROIM), line scans orline profiles, and analyse spectra from the saved datawithout having to reload the sample or redo theacquisition.

Through FSXRM, ordinary spectra can be created off-line from the entire image area, selected matrixregions, lines, and individual points. Full quantitativeanalysis can then be performed on the spectrum usingthe standard EDS analysis software. Elementalintensity maps can be built from the saved data and theelement lists can be changed to create new maps inaddition to those collected initially. Quantitative mapscan be created during post processing using the datasaved with the spectral maps. Each pixel'scorresponding spectrum is recalled for a complete ZAFcorrected quantitative analysis, which is then convertedinto a series of quantitative maps. All of this postprocessing can be performed using the calibration datasaved with the FSXRM or calibration data created fromthe FSXRM itself. High quality quantitative analysismay also be performed due to the longer acquisitiontime for a spectrum that has been obtained from theaddition of many hundreds or even thousands ofindividual spectra, as shown in Figure 3.

1300sec

360sec -

Figure 3: Spectra extracted from a full spectrum map ofa barnacle sample attached to an aluminium hull. Arrows showregions from where the spectra were obtained. The composition of the elements shown on the barnacle spectrum (1300

second spectra) to be Na 0.3wt%, Mg 0.4wt%, Al 0.3wt%, CI 0.03wt%, Sr O.3wt% and S 0.6wt%. BSE image,HWOF=260J.1m,200msec/pixel, 512x5l2, collected over l5hrs at 50kcps and 165eV resolution.

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3. POST PROCESSING OF X-RAY MAPS ANDCHEMICAL IMAGING

Once an x-ray map (XRM) has been collected anumber of analytical software methods can be used toprocess the data and determine further informationabout the microstructure and properties of the material.However, much of the quantitative information isextremely complex and as such is enormously difficultto reproduce as a single image. What is required is amethod that can select multiple quantitativeconcentration criteria and be able to show where this

concentration data exists on a spatial distribution.Through the use of two and three dimensional (2D and3D) scatter (or correlation) diagrams the x-ray imagescan be presented as singular or combinations ofelements [2, 4, 5] (phases to be displayed singularly orsuperimposed over backscattered or secondary electroninformation). Where, scatter diagrams are pixelfrequency versus element concentration profiles plottedagainst each other in two dimensions for selectedelements within the sample, Figures 4e-g.

a. b.

Ti 100 Ti 100

c. d.

Si 100

AlIDaSi 100

e.

~-_._----,'Ibis Peak Contains I AI3 major clusters. ---tThis peak contains2 major clusters.

Ti

All00

h.

Alloof. g.

I.

Figure 4: Titanium alloy bonded to an aluminium alloy by the vacuum casting method [6]. a) SE image of the interfacebetween the two alloys and elemental x-ray maps of b) titanium, c) silicon and d) aluminium. The complete set ofscatter diagrams showing 5 different clusters from the bond interface region: e) titanium versus silicon, f) titaniumversus aluminium, g) silicon versus aluminium, h) scatter diagram generated from the titanium distribution plotted

against the aluminium distribution (The aluminium intensity distribution shows three major intensity peaks, of whichthe left hand peak consists of three major clusters. Similarly, in the titanium intensity distribution, the middle peak

contains two major clusters) and i) pseudo colour map for the elements (Ti-red, AI-blue and Si-green). Maps collectedat 20keY, 512x512 pixel, lOOmsec/pixel and 7kcps. Width offield (WaF) = 288 microns.

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The generation of scatter diagrams is accomplished byconsidering the information given in the linearintensity distribution histogram from each individualelement (Figure 4h). The histograms are created fromthe data contained within each corresponding elementalx-ray map (Figures 4b-d). From this information, atwo-dimensional scatter diagram is produced (Figures4e-g). The clusters observed in the scatter diagramscorrespond to different phases and boundaries withinthe material. The contributing pixels to each cluster canbe used to reconstruct the spatial distribution of itsassociated phase or boundary in a chemical image ofthe specimen.

The larger cluster in Figure 4f is a titanium-aluminiumintermediate phase, which occupies a correspondingarea on the image as shown in Figure 4i (blue-purplecolour).

3.1 Interpretations of Scatter Diagrams

As described in section 3 and Figure 4, scatterdiagrams are element concentration profiles plottedagainst each other and also showing pixel frequency ateach point. From these scatter diagrams we observeclusters, also referred to as nodes, which correspond todifferent chemical phases. We can also observeconnection between clusters (linking) indicating theboundaries between phases within a material. Anothertype of cluster is branching, where there is a link with anode at only one end. This type of cluster is oftenreferring to solid solutions (compositional variations)of the same phase or separate phases of similarchemistry. The contributing pixels to each cluster canbe used to reconstruct the spatial distribution of itsassociated chemical phase or boundary in a chemicalimage of the specimen. In other words, from the scatterdiagram you can select a cluster of points and thendisplay these points redrawn on the electron image.The new image shows the selected analysis pointssuperimposed on the electron image, which wascollected while mapping, giving perfect correlation.The scatter diagrams may be derived from region ofinterest mapping (ROIM) or quantitative XRM(QXRM).

a.

Selecting areas on the scatter diagrams and observingwhere the points lie on the image is a very importantpart of the phase identification process. These selectedanalysis points may then be summed for a moreaccurate analysis in total or by selecting strategic areason the image. Selecting areas on the electron or x-rayimage and showing where these points plot (retrace) onthe scatter diagrams is also important in locatingmissing clusters.

3.2 Ternary and 3D Scatter Diagrams

It is also possible to create ternary scatter plots and 3Dscatter diagrams. The advantage of ternary and 3Dscatter diagrams is that correlations between relatedphases can be visualised in a single diagram (Figure 5).This helps provide more detail that could easily bemissed in 2D diagrams. However, they can also bemisleading, because some details may still be hidden.The way to solve this problem is to use rotating 3Dscatter diagrams that can provide information andreveal facts about a sample that might otherwise havebeen overlooked. Furthermore, software needs to bedeveloped that not only allows three elements to beselected and displayed, but also allows a fourth andfifth element and even a ratio of elements displayed inscatter diagrams.

3.3 Chemical Phase Mapping (CPM)

Through the use of scatter diagrams, the mapping ofphases is possible through selecting the clusters andthen displaying these similar concentration areas on theimage. This is often referred to as phase mapping, butreally it should be called chemical phase mapping(CPM), as phase mapping assumes knowledge ofatomic positions and requires diffraction analysis. TheXRM method that is being described uses chemistrythrough use of either EDS and/or WDS analysis. It isalso important to recognise that all elements need to bemapped, as some phases are determined from veryminor elemental variation and even elements that aredifficult to analyse. If a full spectrum at each pixel issaved, this makes the process of looking for otherelements much easier.

Ti AI

b.

Figure 5: a) Ternary scatter diagram and b) 3D scatter diagram which can be rotated for the results displayed in Figure4.

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Figure 6 shows the phase overlay superimposed on thesecondary electron image, often referred to ascomposite image. The phases have been selected byusing the scatter diagrams and by setting elementalchemistry criteria. This phase map has been createdfrom the images shown in Figure 4. As can be seen,there are also areas on this phase map, which are greyin colour, representing phases that have not beenidentified.

It is best to use as many different methods as possible,such as pseudo colouring, ratio imaging and principalcomponent analysis (PCA) to determine where thephases of interest are. For example, through using

pseudo colouring, where a colour is assigned to aspecific element, the colours of the elements can berotated (changed), which can often reveal furtherinformation (and other phases) in the material. Indeed,the rotating of colours between elements can also showother features, such as hairline cracks, fine precipitatesand small boundary interfaces that would be otherwisemissed. Furthermore, any area that is black on thepseudo colour image indicates that another element isnot represented by the three chosen to form the pseudocolour image, as shown in Figure 2i. Once a phase isdetermined, then the volumetric proportion of eachphase in the sample can be calculated as well as thecompositional range for each phase, Figure 7.

•• Titanium Alloy

Aluminium

•• Si,Ti,AIPhase

•• Si,Al Phaseo Interface

Figure 6: Phase overlay superimposed on the secondary electron image. The phases are selected by setting elementalchemistry criteria. These phases are then assigned a colour, where this colour is then modified by the electron image

grey level information to produce the final image. Results are from sample shown in Figure 4. Maps collected at 20keV,512x512 pixel, lOOmsec/pixel and 7kcps. Width offield (WaF) = 288 microns.

a.

Volumetric.Analysis

5.5%

3.1%

42.6%

20.3%2.4%

0.4%

24.9%

0.8%

b.

Compositional Ranges

• • • • D DAl>U.3 O<Al<12.3 Al>12.3 O<Al<12.3 Al>12.3 0<Al<U.3 Al>12.3 0<A1<12.3Si>11.7 Si>11.7 0<Si<11.7 0<Si<11.7 Si<11.7 8i<11.7 0<Si<11.7 0<Si<11.7Ti>14.3 Ti>14.3 Ti>14.3 Ti>14.3 O<Ti<14.3 O<Ti<14.3 0<Ti<14.3 0<Ti<14.3

c.Figure 7: a) Phase map without overlay, b) volumetric analysis and c) compositional ranges. Results are from sampleshown in Figure 4. Maps collected at 20keV, 512x512 pixel, lOOmsec/pixel and 7kcps. Width offield (WaF) = 288

microns.

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4. CONCLUSION

Interpretation of an image, whether an optical image,back-scattered electron image or a secondary electronimage, is only qualitative. Combining the processes ofimaging with spectroscopy (EDS or WDS) gives thecapabilities for chemical analysis and chemicalmapping on a microscopic scale. The subsequentproduction of elemental maps and chemical phasemaps can make it quantitative.

CoIlecting the fuIl spectrum x-ray map, even if it takes8 to 36 hours to coIlect, is the easy part of the analysis.To completely characterise a sample, a number of post-processing methods, such as elemental mapping,pseudo colouring, ratio mapping, scatter diagramcreation, rotational scatter diagrams and phasemapping should be employed. There is so muchinformation that can be extracted from fuIl spectrummapping that it is easy to get lost in the process.

Materials characterisation is the determination of allaspects of material composition and structure relevantto properties under consideration. This classification istherefore very important to the study and control ofboth a materials property and its processing. Throughthe use of x-ray mapping and post-processingtechniques (chemical imaging), a better understandingof a materials chemical properties and chemical phaseinformation can be determined.

Acknowledgements

Contribution of samples and discussions with DarrenAttard, Paul Huggett, Peter Lloyd and MatthewPhillips are greatly appreciated.

References

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