ARTIFICIAL NEURAL NETWORK ANALYSIS OF

1
ARTIFICIAL NEURAL NETWORK ANALYSIS OF MULTIPLE
IBA SPECTRA
H.F.R.Pinho1,2, A. Vieira2, N.R.Nené1,N. P.
Barradas1,3 1 Instituto Tecnológico e Nuclear,
E.N. 10, 2685 Sacavém, Portugal, 2 ISEP, R. São
Tomé, 4200 Porto, Portugal 3 Centro de Física
Nuclear da Universidade de Lisboa, Avenida Prof.
Gama Pinto 2, 1699 Lisboa, Portugal
1. INTRODUCTION
3. EXPERIMENTAL CONDITIONS
We have previously used artificial neural
networks (ANNs) for automated analysis of
Rutherford backscattering (RBS) data 1,2 One
of the limitations was that one single spectrum
could be analyzed from each sample. When more
than one spectrum is collected, each has to be
analyzed separately, leading to different results
and hence reduced accuracy. Furthermore, often
complementary data are collected, either using
different experimental conditions, or even using
a different technique such as elastic recoil
detection (ERDA) or particle-induced x-ray
analysis. In that case, simultaneous and
self-consistent analysis of all data is
essential. In this work we developed a code
based on ANN to perform the analysis of multiple
RBS and ERDA spectra collected from the same
sample. The ANN developed was applied to a very
simple case determination of the stoichiometry
of TiNOH samples

• Samples
• 15 TiNxOyHz samples were deposited by reactive rf
  magnetron sputtering from a high purity Ti target
  (99.731 ) onto polished high-speed stainless
  steel (AISI M2).
• Spectra
• 35 MeV 35Cl7 ERDA spectra were collected.
• Five spectra were obtained simultaneously from
  each sample one recoil spectrum for each
  element, and the backscattering spectrum from the
  Ti (fig. 2). Conventional data analysis was done
  with the code NDF 4.

4. TRAIN AND TEST SET
ANN Training - 1800 examples corresponding to a
very broad range of possible elemental
concentrations were fed to train the net. These
examples were constructed from simulated
experimental data (fig.3) with Poisson noise
added. The training consisted in 50 iteration
steps. ANN Testing - 200 independent examples
(not used during training) were used to test the
coefficients. A thin carbon layer of varying
thickness was considered, in order to emulate
real-life conditions, where such layers are often
deposited during the experiment due to poor
vacuum conditions.
2. ARTIFICIAL NEURAL NETWORKS

• Recognize recurring patterns in input data,
  without Physics knowledge
• Ideal to do automatically what analysts have
  long done
• relate specific data features to specific
  sample properties
• because spectra can be treated just as
  pictures.
• How? By supervised learning from known
  examples
• -training phase give the ANN many examples
  where the solution is known beforehand
• -let the ANN adjust itself to this training
  set
• -check it against an independent set of data
  the test set
• -do it until no improvement can be found.
• Network Architecture
• (N, I1,.., In, M) (fig.1)
• N-number of inputs
• M-number of outputs
• Ii-number of nodes in hidden layer i
• Present work
• Without pre-processing
• N- 130 yield values (26 channels of each
  spectrum one after the other, including
  leading edge)
• M- 4 (concentration of each element)

6. SUMMARY AND OUTLOOK

• We developed artificial neural networks
  capable of analysing multiple ion beam analysis
  spectra collected from the same sample. The ANNs
  were applied to a simple problem, the
  determination of the stoichiometry of TiNOH
  samples measured with heavy ion ERDA. This
  allowed us to make a thorough study of network
  architecture, connectivity, and effectiveness of
  pre-processing.
• Small networks using the spectral yields with
  no pre-processing achieve the best results in
  real experimental data. However, the ANNs must be
  cluster-linked so that each spectrum is only
  connected to a subset of nodes in the first
  hidden layer exclusively dedicated to that
  spectrum.
• Effective automatic pre-processing (as
  opposed to a-priori pre-processing) is achieved,
  leading to very efficient networks that are easy
  to train.
• We expect this type of architecture to be
  easy to scale up to complex multiple IBA spectra
  problems.
• The authors gratefully acknowledge the financial
  support of FCT under grant POCTI/CTM/40059/2001.

generate
5. RESULTS
erms- root mean square error ? -
relative error average over all samples measured
taking as reference the
results given by NDF (fig.3 and tab.1, 2, 3)
Fully linked networks with pre-processing
Cluster linked networks with and without
pre-processing
Fully linked networks without pre-processing
Architecture erms (train set) erms (test set) e (Ti) e (N) e (O) e (H)
(130,5,4) 0.024 0.025 0.029 0.043 0.049 0.147
(130,15,4) 0.016 0.017 0.032 0.046 0.045 0.081
(130,25,4) 0.016 0.017 0.033 0.046 0.043 0.074
(130,50,4) 0.015 0.016 0.029 0.040 0.046 0.083
(130,10,5,4) 0.017 0.018 0.035 0.051 0.052 0.080
(130,20,10,4) 0.015 0.017 0.029 0.047 0.043 0.097
(130,20,20,4) 0.013 0.015 0.033 0.055 0.051 0.111
(130,30,20,4) 0.015 0.016 0.022 0.047 0.044 0.081
(130,40,20,4) 0.015 0.016 0.030 0.042 0.044 0.081
(130,40,30,4) 0.015 0.015 0.025 0.055 0.046 0.076
(130,50,30,4) 0.016 0.017 0.033 0.037 0.043 0.100
(130,50,40,4) 0.015 0.016 0.022 0.043 0.041 0.058
Architecture erms (train set) erms (test set) e (Ti) e (N) e (O) e (H)
(5,5,4) 0.030 0.029 0.020 0.055 0.044 0.134
(5,15,4) 0.027 0.025 0.022 0.049 0.045 0.093
(5,25,4) 0.027 0.025 0.019 0.052 0.033 0.102
(5,50,4) 0.029 0.027 0.019 0.059 0.038 0.089
(5,10,5,4) 0.031 0.030 0.025 0.043 0.040 0.124
(5,20,10,4) 0.028 0.027 0.021 0.055 0.040 0.097
(5,20,20,4) 0.027 0.026 0.025 0.049 0.045 0.105
(5,30,20,4) 0.037 0.036 0.025 0.051 0.044 0.106
(5,40,20,4) 0.037 0.037 0.023 0.060 0.039 0.109
(5,40,30,4) 0.026 0.026 0.021 0.046 0.035 0.113
(5,50,30,4) 0.027 0.026 0.022 0.046 0.045 0.081
(5,50,40,4) 0.035 0.032 0.021 0.046 0.045 0.097
Architecture erms (train set) erms (test set) e (Ti) e (N) e (O) e (H)
(130,5,4) 0.025 0.024 0.019 0.053 0.037 0.079
(130,10,4) 0.019 0.018 0.027 0.054 0.036 0.054
(130,15,4) 0.019 0.020 0.023 0.059 0.040 0.084
(130,25,4) 0.021 0.021 0.025 0.047 0.042 0.092
(130,50,4) 0.020 0.020 0.029 0.041 0.041 0.107
(5,5,4) 0.033 0.032 0.017 0.057 0.038 0.107
(5,10,4) 0.031 0.030 0.019 0.064 0.036 0.095
(5,15,4) 0.031 0.029 0.018 0.061 0.041 0.077
(5,25,4) 0.031 0.029 0.019 0.062 0.040 0.102
(5,50,4) 0.029 0.028 0.024 0.063 0.033 0.101
Fig.4
1 N. P. Barradas and A. Vieira, Phys. Rev. E62
(2000) 5818. 2 V. Matias, G. Öhl, J.C. Soares,
N. P. Barradas, A. Vieira, P.P. Freitas, S.
Cardoso, Phys. Rev. E 67 (2003) 046705. 3 E.
Alves, A. Ramos, N. P. Barradas, F. Vaz, P.
Cerqueira, L. Rebouta, U. Kreissig, Surf.
Coatings Technology 180-181 (2004) 372 4 N.P.
Barradas, C. Jeynes, and R.P. Webb, Appl. Phys.
Lett. 71 (1997) 291. 5 C. M. Bishop, Neural
Networks for Pattern Recognition (Oxford Oxford
University Press 1995)
Tab.1
Tab.2
Tab.3