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Auger oxygen KLL lineshapes in silicon oxides: pattern recognition analysis
Applied Surface Science 70/71(1993) 299-302 North-Holland
applied surface science
Auger oxygen KLL lineshapes in silicon oxides: pattern recognition analysis J. Zemek a,*, T. Vystrcil a, B. Lesiak-Orlowska
b and A. Jablonski b
aInstituteof Physics, Czech Academy of Sciences, Cukrovarnicka IO, 162 00 Prague 6, Czech Republic b Institute of Physical Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52,
01-224 Warsaw, Poland
Received 21 September 1992; accepted for publication 7 October 1992
The pattern recognition method was applied to the oxygen KLL Auger electron spectra of in situ prepared SiO, SiO, as standards and native silicon oxides grown on Si(lll), SiO and silicon nitride films exposed to air. This method of data evaluation enables us to recognize the chemical state of oxygen from the shape of the 0 KLL spectra recorded for standards and real silicon oxide surfaces. Results show that the 0 KLL lineshape recorded for the as-grown and annealed native oxides on Si(ll1) is nearly the same as for the SiO standard. For the remaining real silicon oxide surfaces the oxygen bonding is more complex with a certain similarity between the 0 KLL spectra and the corresponding spectra of SiO,. Recognized changes in the 0 KLL lineshapes cannot be explained within a mixture model, but by a random bonding model or by a model which assumes that SiO has a unique structure.
1. Introduction
Much effort has been devoted to the SiLW lineshapes in studies of the chemical bonding of the Si-0 system [l]. Although interesting results have been achieved, the evaluation of SiLW spectra is rather complicated. The use of OKLL spectra for the same purpose is scarce as the almost atomic-like OKLL lineshape [ll appears to be relatively insensitive to chemical bonding in the Si-0 system. This is due to the same nearest neighbourhoods of 0 atoms in different SiO, (x < 2) materials, as deduced from commonly used random bonding (RB) or mixture models [21, considering fourfold-bonded Si and twofoldbonded 0. Recently, the OIU,L, and OKL,,,Lzs intensity ratio has been used to elucidate the adsorption geometry of oxygen at the Si(OO1)surface [3]. Apart from the interpretation of the ratio, based on the degree of charge transfer [4] or the Si-0 bond distance [3,5], it is obvious that the OKLL * To whom correspondence 0169-4332/93/$06.00
should be addressed.
lineshape is generally convenient for identification of the chemical bonding. Changes observed in the OKLL lineshapes are, however, subtle. In this paper the pattern recognition (PR) method is used to differentiate the slight changes in recorded spectra and to identify the possible oxygen bonding states in different silicon oxides and nitrides exposed to air on the basis of carefully recorded spectra for well defined SiO and SiO, standards.
2. The pattern recognition method Methods of the PR were frequently used in solving numerous problems in chemistry [6]. Recently, a PR method in connection with AES has been applied in the identification of different chemical forms of C and In atoms from the shape of CKLL [7] and InMNN [S] spectra. A brief outline of the PR method is given below. All the involved mathematical formalism is omitted here and can be found in ref. 191. The application of the PR method requires
0 1993 - Elsevier Science Publishers B.V. All rights reserved
300
J. Zemek et al. / Auger OKLL lineshapes in silicon oxides
treatment of the spectra at four stages: (1) Acquisition and creation of the training set of standards containing the spectra of well defined materials. (2) Preliminary spectra treatment: (i) modification of intensities to have unit peak-to-peak height, (ii) shift on the energy scale to a common position of minima. (3) Selection of the most effective recognition procedure on the basis of the training set of standards. (4) Acquisition and identification of spectra of unknown materials on the basis of the training set of standards. Any object (e.g. OKLL spectra) analyzed by the PR method is represented by the n-dimensional vector with components called features (contents of channels in which spectra are acquired). Similar objects create clusters of points in the n-dimensional space (classes). One of the most universal methods of classification is the k-nearest neighbour (kNN) rule. The kNN rule identifies an unknown spectrum by analyzing the immediate neighbourhood of the considered vector in the n-dimensional space. The vector is ascribed to a class which is represented by the greatest number of vectors among the k-nearest neighbours. The aim of the classification is to identify unknown vectors with the smallest uncertainty which is described as probability of misclassification A E [9].
hanced CVD Si,N, on SKlll), (Si3Ny); silicon nitride films (Si,Ny) deposited by electron beam evaporation of Si and, at the same time, nitrogen ion implantation using the ion beam energy in the range 60-92 keV, and the ratio of fluxes of incoming N to Si atoms 0.1-4.4 (see table 2).
4. Results and discussion
The OKLL spectra of standards and selected samples are shown in fig. 1. The spectra are normalized to obtain a constant peak-to-peak height and are shifted on the energy scale to a common position of minima. As the spectra are very similar, it is hardly possible to find difference from a visual inspection of the spectra. The PR procedure was applied to the preprocessed data in two steps. Firstly, the 0 KLL spectra recorded for SiOF and SiO’” were considered as a training set. The spectra recorded for asgrown and annealed Si(llW samples were ascribed to SiO’“, while the spectra of remaining samples to SiOF. Secondly, we included into the training set the spectra recorded for SiO”“, to obtain fine-drawn resolution. The SiO”” is exposed to air, hence, more complex oxygen bonding (e.g. 0-Si, O-H, O-C) is expected. Similar
3. Experimental AES measurements were performed using the Varian AES/LEED-120 system equipped with a cylindrical mirror analyzer and an integral electron gun. The OKLL spectra were recorded as the first derivative of the energy distribution using a primary beam energy of 1200 eV, a beam current of 1 x lop6 A and 2 V,., modulation voltage. For the PR purposes twenty 0 KLL spectra from each analyzed surface were taken. The following materials were used as standards for the analysis: in situ evaporated SiO on SXlll), (SiO’“); evaporated SiO on SXlll) exposed to air, (SiOex); silica glass rod, in situ broken under UHV conditions, (SiO$“). The following series of samples exposed to air were selected: SK111) wafers, (Si(l1 l)?; plasma-en-
Fig. 1. Sets of twenty OKLL spectra recorded for standards (a-c) and selected samples (d-f): (a) SiOF, (b) SiO’“, (c) SiO’“, Cd) Si(lll)ex, (e) Si,N,e”, (f) Si,Ny. The spectra are normalized to obtain a constant peak-to-peak height and shifted on the energy scale to a common position of minima.
J. Zemek et al. /Auger OKLL lineshapes in silicon oxides Table 1 Classifier based on OKLL (SiO’“, SiOex, SiOF) Standards
Number features
of
spectra
recorded
Optimum number of nearest neighbours,
for
standards
Misclassification probability, AE
2 0
120 120 120
-
SiOeX k_
L 0 E :
0 0 0.025
38 38 14
SiOF
z
k wt SiO’“, SiOe” SiO’“, SiOF SiOe”, SiO’”2
301
I-
SiOex
SiOi”
0
5 Distance
10
20
15
bonding states of oxygen atoms likely occur at the surfaces of measured samples. The preprocessing of data from the training set was also performed to illustrate clustering of a set of standards. Histograms of the distances were constructed. For each pair of standards a straight line joining the centres of gravity was calculated. Then, all vectors from two sets were projected on this line and the distances along this line from arbitrarily selected points to the point of projection were evaluated. Histograms of distances show the clustering of vectors in 120-dimensional space (fig. 2). Well separated clustering is obvious and good performance of classifiers is expected. Then,
the parallel network of binary classifiers was developed on the basis of the training set (table 1). The classifier based on 120 features recognizes different shapes of OKLL spectra without error for SiO’“, SiO”” and SiO’“, SiOy pairs and with AE = 0.025 for the SiO”“, SiOF pair. The classifier was applied in the identification of the 0 KLL spectra of the samples. The results are listed in table 2.
Table 2 Results of classification
of Si(lllY,
of the oxygen chemical
state at the surface
(arbitrary
units)
Fig. 2. Histograms of distances along the straight line joining . . centres of gravity of two groups of clusters in 120-dimenstonat space for OKLL spectra in different chemical environment.
Sample
Sample
treatment/remarks
1. 2.
Si(lll)ex Si(lll)ex
3.
Si(ll1)”
4. 5.
Si,Ni Si,Ni
As-grown Annealed at 300°C for 200 min under UHV Annealed at 700°C for 60 min under UHV As-grown After 2000 eV electron bombardment with 7 X lo*’ cm-’ electron dose
6. 7. 8. 9. 10.
Si,N,” Si,Nt Si,Nc Si,N; Si,NE
Si,N,eX and Si,N,eX samples Recognized
chemical
state
SiO’” + [SiO”‘] SiO’” + [SiOex] SiO’” + [SiOex] SiOeX SiOy + SiO””
A
E
N/Si
45 80 45 80 45
60000 92000 60000 92000 80000
2.4 1.4 4.4 0.2 0.8
Si? SiO” + [SiOF] SiOe” SiO”” + [SiOF] SiO””
In the case of more than one value appearing as the result of identification, the first value refers to the predominant result. The values presented in the brackets occur less frequently. A, E and N/Si denote the angle between the ion beam and the normal to the surface, the ion beam energy in eV, and the ratio of fluxes of incoming N to Si atoms, respectively.
302
J. Zemek et al. /Auger
OKLL lineshapes in silicon oxides
Recognized OKLL spectra recorded for the surfaces of as-grown and annealed Si(ll1)“” samples are nearly the same as that for the SiO’“. The result can be understood considering a thin (< 1 nm) native oxide layer on SK1111 (as deduced from the Si LVV oxide/ substrate intensity ratio), where three-dimensional (3D) SiO, oxide has not yet developed. Note, no modification in OKLL spectral shape is recognized due to heat treatment. It means that the native oxide is sufficiently stable at least up to 700°C. The result is consistent with the literature data. Observed chemical shifts of Si 2p lines for thin native oxides do not reach the value for 3D SiO, [lo]. The pulse-laser atom probe method points to a very thin (N 0.5 nm) intermediate layer of stoichiometric SiO between the silica and silicon in thermal oxides [ll]. A similar conclusion is drawn from the depth profiling of the Si/SiO, interface. The SiO is found mostly located at the Si/SiO, interface 1121. The OKLL spectra for the as-grown Si,N,” sample are recognized as the OKLL spectra of SiOe”, most likely due to a long exposure to air. After high-dose electron bombardment of the surface, the spectra change their shape towards that of SiO,. The OIUL spectra of the Si,Nz samples are ascribed mostly to SiO”” with one exception of sample #6, where the spectra are clearly ascribed to SiOF. Here, no correlation with deposition conditions is apparent. There is an important question of whether recognized changes in the OKLL spectra can be understood in the frame of the existing structural models [2]. Within the mixture model, OKLL lineshapes of SiO, are expected exactly the same as for SiO,. The RB model follows the same conclusion because the number of nearest neighbours of oxygen atoms in SiO, is the same. However, this is not generally valid for the second neighbours. Also a transport of Auger electrons in the near-surface regions can affect resulting lineshapes [l]. An alternative possible interpreta-
tion is based on recent experimental results which indicate that the SiO has a unique structure [13]. In this case recognized differences between SiO and SiO, OKLL spectra can reflect the distinguished structures. In conclusion, it is demonstrated that AES together with the pattern recognition method is able to recognize slight changes in the lineshapes hardly observable by visual inspection of spectra. The OKLL spectra recorded for as-grown and annealed native oxide layers on SK1111 are ascribed to SiO’” standard spectra, indicating the structural similarity between them. The spectral shapes of remaining samples exposed to air are (more or less) approaching those of the standard SiOF samples. Recognized changes in the 0 KLL lineshapes can be understood within the RB model or by a model which assumes that SiO has a unique structure.
References
111D.E.
Ramaker, Crit. Rev. Solid State Mater. Sci. 17 (1991) 211, and references therein. PI K. Hiibner, Phys. Status Solidi 61 (1980) 665. Surf. Sci. 260 (1992) 23. 131 E.G. Keim and H. Wormeester, Solid State Commun. 31 (1979) 347. 141 R. Weissmann, H.J. Borg and A. van Silfhout, Surf. Sci. [51 H. Wormeester, 258 (1991) 197. in Chemistry (Springer, t61 K. Varmuza, Pattern Recognition Berlin, 1980). A. Jablonski and A. Jozwik, [71 B. Lesiak, M. Zagorska, Surf. Inter. Anal. 12 (1988) 461. and A. Jablonski, J. ElecPI J. Zemek, B. Lesiak-Orlowska tron Spectrosc. Rel. Phen. 60 (1992) 13. of Pattern Recogni[91 T.Y. Young and K.S. Fu, Handbook tion and Image Preprocessing (Academic Press, Orlando, FL, 1986). and P.J. Grunthaner, Mater. Sci. Rep. 1 mJ1 F.J. Grunthaner (1986) 69. C.R.M. Grovenor and A. Cerezo, Phil. [ill A.M. Stoneham, Mag. B 55 (1987) 201. [121 T. Suzuki, M. Muto, M. Hara, K. Yamabe and T. Hattori, Jpn. J. Appl. Phys. 25 (1986) 544. [131 T. Scimeca, Solid State Commun. 77 (1991) 817.
applied surface science
Auger oxygen KLL lineshapes in silicon oxides: pattern recognition analysis J. Zemek a,*, T. Vystrcil a, B. Lesiak-Orlowska
b and A. Jablonski b
aInstituteof Physics, Czech Academy of Sciences, Cukrovarnicka IO, 162 00 Prague 6, Czech Republic b Institute of Physical Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52,
01-224 Warsaw, Poland
Received 21 September 1992; accepted for publication 7 October 1992
The pattern recognition method was applied to the oxygen KLL Auger electron spectra of in situ prepared SiO, SiO, as standards and native silicon oxides grown on Si(lll), SiO and silicon nitride films exposed to air. This method of data evaluation enables us to recognize the chemical state of oxygen from the shape of the 0 KLL spectra recorded for standards and real silicon oxide surfaces. Results show that the 0 KLL lineshape recorded for the as-grown and annealed native oxides on Si(ll1) is nearly the same as for the SiO standard. For the remaining real silicon oxide surfaces the oxygen bonding is more complex with a certain similarity between the 0 KLL spectra and the corresponding spectra of SiO,. Recognized changes in the 0 KLL lineshapes cannot be explained within a mixture model, but by a random bonding model or by a model which assumes that SiO has a unique structure.
1. Introduction
Much effort has been devoted to the SiLW lineshapes in studies of the chemical bonding of the Si-0 system [l]. Although interesting results have been achieved, the evaluation of SiLW spectra is rather complicated. The use of OKLL spectra for the same purpose is scarce as the almost atomic-like OKLL lineshape [ll appears to be relatively insensitive to chemical bonding in the Si-0 system. This is due to the same nearest neighbourhoods of 0 atoms in different SiO, (x < 2) materials, as deduced from commonly used random bonding (RB) or mixture models [21, considering fourfold-bonded Si and twofoldbonded 0. Recently, the OIU,L, and OKL,,,Lzs intensity ratio has been used to elucidate the adsorption geometry of oxygen at the Si(OO1)surface [3]. Apart from the interpretation of the ratio, based on the degree of charge transfer [4] or the Si-0 bond distance [3,5], it is obvious that the OKLL * To whom correspondence 0169-4332/93/$06.00
should be addressed.
lineshape is generally convenient for identification of the chemical bonding. Changes observed in the OKLL lineshapes are, however, subtle. In this paper the pattern recognition (PR) method is used to differentiate the slight changes in recorded spectra and to identify the possible oxygen bonding states in different silicon oxides and nitrides exposed to air on the basis of carefully recorded spectra for well defined SiO and SiO, standards.
2. The pattern recognition method Methods of the PR were frequently used in solving numerous problems in chemistry [6]. Recently, a PR method in connection with AES has been applied in the identification of different chemical forms of C and In atoms from the shape of CKLL [7] and InMNN [S] spectra. A brief outline of the PR method is given below. All the involved mathematical formalism is omitted here and can be found in ref. 191. The application of the PR method requires
0 1993 - Elsevier Science Publishers B.V. All rights reserved
300
J. Zemek et al. / Auger OKLL lineshapes in silicon oxides
treatment of the spectra at four stages: (1) Acquisition and creation of the training set of standards containing the spectra of well defined materials. (2) Preliminary spectra treatment: (i) modification of intensities to have unit peak-to-peak height, (ii) shift on the energy scale to a common position of minima. (3) Selection of the most effective recognition procedure on the basis of the training set of standards. (4) Acquisition and identification of spectra of unknown materials on the basis of the training set of standards. Any object (e.g. OKLL spectra) analyzed by the PR method is represented by the n-dimensional vector with components called features (contents of channels in which spectra are acquired). Similar objects create clusters of points in the n-dimensional space (classes). One of the most universal methods of classification is the k-nearest neighbour (kNN) rule. The kNN rule identifies an unknown spectrum by analyzing the immediate neighbourhood of the considered vector in the n-dimensional space. The vector is ascribed to a class which is represented by the greatest number of vectors among the k-nearest neighbours. The aim of the classification is to identify unknown vectors with the smallest uncertainty which is described as probability of misclassification A E [9].
hanced CVD Si,N, on SKlll), (Si3Ny); silicon nitride films (Si,Ny) deposited by electron beam evaporation of Si and, at the same time, nitrogen ion implantation using the ion beam energy in the range 60-92 keV, and the ratio of fluxes of incoming N to Si atoms 0.1-4.4 (see table 2).
4. Results and discussion
The OKLL spectra of standards and selected samples are shown in fig. 1. The spectra are normalized to obtain a constant peak-to-peak height and are shifted on the energy scale to a common position of minima. As the spectra are very similar, it is hardly possible to find difference from a visual inspection of the spectra. The PR procedure was applied to the preprocessed data in two steps. Firstly, the 0 KLL spectra recorded for SiOF and SiO’” were considered as a training set. The spectra recorded for asgrown and annealed Si(llW samples were ascribed to SiO’“, while the spectra of remaining samples to SiOF. Secondly, we included into the training set the spectra recorded for SiO”“, to obtain fine-drawn resolution. The SiO”” is exposed to air, hence, more complex oxygen bonding (e.g. 0-Si, O-H, O-C) is expected. Similar
3. Experimental AES measurements were performed using the Varian AES/LEED-120 system equipped with a cylindrical mirror analyzer and an integral electron gun. The OKLL spectra were recorded as the first derivative of the energy distribution using a primary beam energy of 1200 eV, a beam current of 1 x lop6 A and 2 V,., modulation voltage. For the PR purposes twenty 0 KLL spectra from each analyzed surface were taken. The following materials were used as standards for the analysis: in situ evaporated SiO on SXlll), (SiO’“); evaporated SiO on SXlll) exposed to air, (SiOex); silica glass rod, in situ broken under UHV conditions, (SiO$“). The following series of samples exposed to air were selected: SK111) wafers, (Si(l1 l)?; plasma-en-
Fig. 1. Sets of twenty OKLL spectra recorded for standards (a-c) and selected samples (d-f): (a) SiOF, (b) SiO’“, (c) SiO’“, Cd) Si(lll)ex, (e) Si,N,e”, (f) Si,Ny. The spectra are normalized to obtain a constant peak-to-peak height and shifted on the energy scale to a common position of minima.
J. Zemek et al. /Auger OKLL lineshapes in silicon oxides Table 1 Classifier based on OKLL (SiO’“, SiOex, SiOF) Standards
Number features
of
spectra
recorded
Optimum number of nearest neighbours,
for
standards
Misclassification probability, AE
2 0
120 120 120
-
SiOeX k_
L 0 E :
0 0 0.025
38 38 14
SiOF
z
k wt SiO’“, SiOe” SiO’“, SiOF SiOe”, SiO’”2
301
I-
SiOex
SiOi”
0
5 Distance
10
20
15
bonding states of oxygen atoms likely occur at the surfaces of measured samples. The preprocessing of data from the training set was also performed to illustrate clustering of a set of standards. Histograms of the distances were constructed. For each pair of standards a straight line joining the centres of gravity was calculated. Then, all vectors from two sets were projected on this line and the distances along this line from arbitrarily selected points to the point of projection were evaluated. Histograms of distances show the clustering of vectors in 120-dimensional space (fig. 2). Well separated clustering is obvious and good performance of classifiers is expected. Then,
the parallel network of binary classifiers was developed on the basis of the training set (table 1). The classifier based on 120 features recognizes different shapes of OKLL spectra without error for SiO’“, SiO”” and SiO’“, SiOy pairs and with AE = 0.025 for the SiO”“, SiOF pair. The classifier was applied in the identification of the 0 KLL spectra of the samples. The results are listed in table 2.
Table 2 Results of classification
of Si(lllY,
of the oxygen chemical
state at the surface
(arbitrary
units)
Fig. 2. Histograms of distances along the straight line joining . . centres of gravity of two groups of clusters in 120-dimenstonat space for OKLL spectra in different chemical environment.
Sample
Sample
treatment/remarks
1. 2.
Si(lll)ex Si(lll)ex
3.
Si(ll1)”
4. 5.
Si,Ni Si,Ni
As-grown Annealed at 300°C for 200 min under UHV Annealed at 700°C for 60 min under UHV As-grown After 2000 eV electron bombardment with 7 X lo*’ cm-’ electron dose
6. 7. 8. 9. 10.
Si,N,” Si,Nt Si,Nc Si,N; Si,NE
Si,N,eX and Si,N,eX samples Recognized
chemical
state
SiO’” + [SiO”‘] SiO’” + [SiOex] SiO’” + [SiOex] SiOeX SiOy + SiO””
A
E
N/Si
45 80 45 80 45
60000 92000 60000 92000 80000
2.4 1.4 4.4 0.2 0.8
Si? SiO” + [SiOF] SiOe” SiO”” + [SiOF] SiO””
In the case of more than one value appearing as the result of identification, the first value refers to the predominant result. The values presented in the brackets occur less frequently. A, E and N/Si denote the angle between the ion beam and the normal to the surface, the ion beam energy in eV, and the ratio of fluxes of incoming N to Si atoms, respectively.
302
J. Zemek et al. /Auger
OKLL lineshapes in silicon oxides
Recognized OKLL spectra recorded for the surfaces of as-grown and annealed Si(ll1)“” samples are nearly the same as that for the SiO’“. The result can be understood considering a thin (< 1 nm) native oxide layer on SK1111 (as deduced from the Si LVV oxide/ substrate intensity ratio), where three-dimensional (3D) SiO, oxide has not yet developed. Note, no modification in OKLL spectral shape is recognized due to heat treatment. It means that the native oxide is sufficiently stable at least up to 700°C. The result is consistent with the literature data. Observed chemical shifts of Si 2p lines for thin native oxides do not reach the value for 3D SiO, [lo]. The pulse-laser atom probe method points to a very thin (N 0.5 nm) intermediate layer of stoichiometric SiO between the silica and silicon in thermal oxides [ll]. A similar conclusion is drawn from the depth profiling of the Si/SiO, interface. The SiO is found mostly located at the Si/SiO, interface 1121. The OKLL spectra for the as-grown Si,N,” sample are recognized as the OKLL spectra of SiOe”, most likely due to a long exposure to air. After high-dose electron bombardment of the surface, the spectra change their shape towards that of SiO,. The OIUL spectra of the Si,Nz samples are ascribed mostly to SiO”” with one exception of sample #6, where the spectra are clearly ascribed to SiOF. Here, no correlation with deposition conditions is apparent. There is an important question of whether recognized changes in the OKLL spectra can be understood in the frame of the existing structural models [2]. Within the mixture model, OKLL lineshapes of SiO, are expected exactly the same as for SiO,. The RB model follows the same conclusion because the number of nearest neighbours of oxygen atoms in SiO, is the same. However, this is not generally valid for the second neighbours. Also a transport of Auger electrons in the near-surface regions can affect resulting lineshapes [l]. An alternative possible interpreta-
tion is based on recent experimental results which indicate that the SiO has a unique structure [13]. In this case recognized differences between SiO and SiO, OKLL spectra can reflect the distinguished structures. In conclusion, it is demonstrated that AES together with the pattern recognition method is able to recognize slight changes in the lineshapes hardly observable by visual inspection of spectra. The OKLL spectra recorded for as-grown and annealed native oxide layers on SK1111 are ascribed to SiO’” standard spectra, indicating the structural similarity between them. The spectral shapes of remaining samples exposed to air are (more or less) approaching those of the standard SiOF samples. Recognized changes in the 0 KLL lineshapes can be understood within the RB model or by a model which assumes that SiO has a unique structure.
References
111D.E.
Ramaker, Crit. Rev. Solid State Mater. Sci. 17 (1991) 211, and references therein. PI K. Hiibner, Phys. Status Solidi 61 (1980) 665. Surf. Sci. 260 (1992) 23. 131 E.G. Keim and H. Wormeester, Solid State Commun. 31 (1979) 347. 141 R. Weissmann, H.J. Borg and A. van Silfhout, Surf. Sci. [51 H. Wormeester, 258 (1991) 197. in Chemistry (Springer, t61 K. Varmuza, Pattern Recognition Berlin, 1980). A. Jablonski and A. Jozwik, [71 B. Lesiak, M. Zagorska, Surf. Inter. Anal. 12 (1988) 461. and A. Jablonski, J. ElecPI J. Zemek, B. Lesiak-Orlowska tron Spectrosc. Rel. Phen. 60 (1992) 13. of Pattern Recogni[91 T.Y. Young and K.S. Fu, Handbook tion and Image Preprocessing (Academic Press, Orlando, FL, 1986). and P.J. Grunthaner, Mater. Sci. Rep. 1 mJ1 F.J. Grunthaner (1986) 69. C.R.M. Grovenor and A. Cerezo, Phil. [ill A.M. Stoneham, Mag. B 55 (1987) 201. [121 T. Suzuki, M. Muto, M. Hara, K. Yamabe and T. Hattori, Jpn. J. Appl. Phys. 25 (1986) 544. [131 T. Scimeca, Solid State Commun. 77 (1991) 817.