NAN/PHY/MSE 546 (Venables) Sect 3.4

## Notes for NAN/PHY/MSE 546 Sect 3.4 (Venables)

### © Arizona Board of Regents for Arizona State University and John A. Venables

Lecture notes by John A. Venables, revised for Spring 2005. Latest version 26 August 2012, reformatted.

Refs: Review and specialist articles and books referred to in the text (see list in the previous section). This section has been expanded slightly in my book, chapter 3.4.

## 3.4 Quantification of Auger Spectra

### General Equations describing Quantification

The general equation governing the Auger electron current, IA caused by a probe current Ip can be written down straightforwardly, but is not immediately transparent, and really needs to be explained using a schematic drawing, such as diagram 113. The incoming electron causes an electron cascade below the surface, whose spatial extent is typically much greater than the imfp. For example, the spatial extent is about 0.5 μm at an incident energy Eo = 20 keV, and also depends on the angle of incidence, θo. As a result Auger electrons can be produced by the incoming primary electron beam, and also by the backscattered electrons as they emerge from the sample; the Auger signal intensity thus contains the backscattering factor, R, which is a function of the sample material, Eo and θo.

The ratio IA/Ip is a product of terms describing the production and detection of the Auger electrons. The Auger yield Y is the number of Auger electrons emitted into the total solid angle (Ω = 4π sterad). It is therefore not dependent on the details of the analyser. The detection efficiency D of the analyser can be written as (T.ε), where T is a function f(Ωa/4π), Ωa being the solid angle collected by the analyser, and ε is f(ΔE/E), most simply = (ΔE/E). Thus

IA/Ip = Y.D = [σγR]. secθo.Ne.(T.ε).

Here we have Y expressed as the cross-section for the initial ionization event (σ), the Auger efficiency (γ), discussed in outline in section 3.3, and the factor R. The secθo term describes the extra ionization path length caused by having the primary beam at an angle to the sample normal. Finally Ne is the effective number of atoms/unit area contributing to the (particular) Auger process.

What we actually want to know is: given a measured signal IA, how many A-atoms are there on the surface? Typically there is not a unique answer to such a simple question, because the signal depends not only on the number of atoms but also on their distribution in depth (diagram 114). There are two cases which can be solved uniquely, which are instructive in showing how such analyses work. The first is when all the atoms are in the surface layer: then Ne = N1, and if one knew all the other terms in the equation, we could determine N1. The second case is when the atoms are uniformly distributed in depth: in this case we can show that Ne = Nm, where Nm is the bulk (3D) density of A-atoms. The proof of the second case is as follows. We work out

where the integral is over the two analyzer angles (θa, φa) and depth z. The path length in the sample in the direction of the analyzer is z/cosθa, so we are assuming exponential attenuation without change of direction; this is reasonable for inelastic scattering of the outgoing electrons. Because N(z) is constant = Nm, we can do the integration over z first. This gives

(Ne.T) = λNm.(1/4π)∫∫ cosθa.sinθaaa = λNm.f(θa, φa).

The angular double integral is just the cosine electron emission distribution, integrated over the solid angle of the analyzer; so f = T, Ne = Nm, as required.

A detailed study of the Auger signals from bulk elements has been performed by D.R. Batchelor et al., Surface and Interface Analysis 14 (1989) 700-716, from which diagrams 115 and 116 are taken. These studies show that the dependence of the Auger peak height on primary beam energy Eo explores the variation of (σR); the variation with θo is determined by (Rsecθo.T); and the dependence on atomic number Z is influenced by σ, γ, R, Nm and ε. To make these comparisons we need to take a particular ionization cross-section, and also to calculate the Auger backscattering factor R = 1 + r, where r is defined as

r = (1/σ(Eo)secθo)∫∫σ(E) (d2η/dEdθ)sinθdθdE,

where the normal electron backscattering factor η is given by

η = ∫∫(d2η/dEdθ)sinθdθdE.

This means that the Auger backscattering factor can be written as R = 1 + βη, where the factor β is typically greater than 1, and depends on the energy and angular distribution of backscattered electrons, and the Auger energy in relation to the beam energy via the relevant cross sections. The detailed study of the basic quantification equation thus leads to some interesting physics, and maybe eventually to a determination of one or more parameters of the experiment, but it does not lead directly to easy analysis of samples. For this we have to keep many of the experimental parameters fixed, and use standard samples for comparison.

### Ratio Techniques

Ratio techniques are common in all forms of quantitative analysis, principally because they allow one to eliminate instrumental variables. The measurement is then the value of (IA/Ip) for the sample (s), ratioed to the same quantity for the (pure element) standard (el). Comparing the terms in the quantification equation, we can see that the only terms which do not cancel out, for bulk, uniformly distributed samples, are

(IA/Ip)s/(IA/Ip)el = (Rsλs/Rel λel).(Ns/Nel);

the last bracket is what we want to know, and the previous term is a matrix dependent factor. Without detailed calculation it is not obvious how such terms behave, but they can sometimes vary slowly. For example, P.H. Holloway (University of Florida) and his group have studied several binary alloy systems, and shown that the matrix dependent factors often vary linearly with composition. If one is stuck, one could establish standards closer to the composition of interest. This might be seen as a last resort: it is clear that the smaller extrapolation you have to perform, the more accurate the result is likely to be.

Many authors have developed programs for studying such effects, and various national standards organizations (NIST, NPL, etc) have got involved. The author most concerned in the USA is C.J. Powell of NIST, who has written extensively on this topic; his UK counterpart is M.P. Seah, who works for NPL. It is clearly no accident that they have concerned themselves with these aspects of quantitative analysis, and indeed run conferences with the title 'Quantitative Surface Analysis', or journals such as 'Surface and Interface Analysis' at/in which such matters are discussed in great detail. There are also major consulting businesses based on such analyses, since the services and expertise are often very expensive to maintain 'in-house'; the best known may be C.A. Evans and Associates. I do not propose to discuss these topics further here, but the flavor of this work can be obtained from the specialist books, especially G.C. Smith's; he too worked for NPL, and gives a list of significant papers by the above authors and others. Some of these resources are featured on my Surface Analysis page.

A typical 'surface science' application is to layer by layer growth. This is a case which you can work out for yourself; the experimental ratio is (IA/Ip) for a multilayer divided by (IA/Ip) for a ML. Here we assume that there are N1 atoms in the first layer, N2 in the second and so on, and their spacing is d. Then we can work out the signals at coverage between n and n+1 ML from both the layers and the substrate, as sums which take into account the attenuation. For example, if n = 1, and we neglect attenuation within the first layer

Ne = N1((2-θ) + (θ-1) exp(-d/λcosθe)) + N2,

where the first (second) term in brackets correspond to the proportion of the first layer which is uncovered (covered) by the second layer, and so on. This relation leads to a series of straight lines in curves for layer growth, often plotted as a function of deposition time (diagram 111), from which the imfp λ can be deduced in favorable cases.

The simplest case, where there are the same numbers of atoms in each completed ML, can be worked out explicitly. In that case, the slope of the second ML curve ratioed to that of the first ML gives exp(-d/λcosθe), from which d/λ can be extracted if the effective analyser angle θe is known. This effective angle is given by

cosθe = (1/Ωa)∫∫cosθa.sinθ aaa,

with the integrals taken over the analyzer acceptance. For detailed studies, it is advisable to construct computer programs which take the analyzer geometry into account, and then perform the angular integration numerically. The purists among you should note that what is determined experimentally is known as the attenuation length; the discussion of how elastic (wide angle) scattering influences the relation of experiment to the imfp is ongoing (see Smith, section 5.3, pages 58-61 and refs quoted).

An example of a layer growth analysis is shown in diagram 112, which shows both the Auger spectra for a series of Ag deposits on Si(100) and the corresponding Auger curves as a function of coverage at both room (RT) and elevated temperature. This is a case where the growth follows the ‘layer plus island’, or Stranski-Krastanov mode. From the Auger curves we see that at RT, the layer growth is followed for approximately 2ML, but then they diverge, indicating roughening or islanding. The high T behavior is much more extreme, with a first layer which is less than 0.5 ML thick, and islands grown on top of, and in competion with, this dilute layer. The Ag/Si(111) and Ag/Ge(111) systems have also been studied with the root-3 reconstructed layer at high temperatures having a thickness of around 1 ML. The exact coverage of such layers has been surprisingly controversial, especially when the layers are only slightly more stable than the islands.

There are now many examples, from this and other growth systems, which show characteristic Auger curves associated with layer and island growth. The purpose of a detailed layer growth analysis is often to show that the system does not follow the layer growth mode. It is only by detailed quantitative analysis, with for example known mean free paths, that one can make such definitive statements from Auger curves on their own. An example in which I was involved is J.A. Venables et al., J. Phys D 29 (1996) 240-245, with corrections in J. Phys D 30 (1997) 3163-5, and more examples in R. Persaud et al., Surface Sci. 401 (1998) 12-21. In this case we were using the layer growth analysis to investigate surface segregation in Si/Ge/Si(100) and inter-diffusion in Fe/Ag/Fe(110) hetero-structures.

You might note that many authors have used deposition time as the dose variable, and then simply state that the break point corresponds to 1 ML coverage; this is poor logic, since the coverage is the independent variable and the Auger intensity is the dependent variable. Used in the way described here, the Auger curve is not calibrated absolutely; it needs to be calibrated indepentently, e.g. via a calibrated quartz oscillator or Rutherford Backscattering Spectroscopy (RBS), as discussed, for example, by Feldman and Mayer.

A possible mini-project on Surface Analysis: Write computer programs for the Auger intensity as a function of coverage for element A growing on element B in the following cases. The effective mean free paths are λAA for Auger line A at Energy EA in element A, λAB for Auger line A at Energy EA in element B, with λBA and λBB defined analogously.

(a) "Almost" layer by layer growth, neglecting backscattering effects, where all layers have the same atomic density, N0, except that the first layer has a different density N1 that is < N0.

(b) Stranski-Krastanov growth, where islands of atomic density N0 and height h grow on top of the first layer with density N1 that is < N0. What can you say about backscattering effects if the backscattering coefficient of element B is greater than that of element A?

Continue to section 3.5