NEUTRON DEPTH PROFILE CALCULATIONS USING ARTIFICIAL NEURAL NETWORKS

K Hossny,A Magdy,S Magdi,Y Yasser,F Nasr,M Margulis,P Blaise

doi:10.1051/epjconf/202124706046

K Hossny, A Magdy + Show 5 more

Open Access

https://doi.org/10.1051/epjconf/202124706046

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Abstract

Neutron depth profiling (NDP) is a non-destructive technique used for identifying the concentration of impurity isotopes below the sample surface. NDP is carried out by detection of the emitted charged particles resulting from bombarding the sample with neutrons. NDP specifies the isotopic concentration versus the sample depth for a few micrometers below the surface. The sample is bombarded inside a research reactor using a thermal neutron beam. Charged particles like alpha particles or protons are produced from the neutron induced reactions in the sample. Each neutron isotopic interaction produces a certain Q, indicating a specific kinetic energy for the emitted charged particle. As the charged particle travels through the sample to eject the surface, it loses energy to atoms (electrons) on its path. The charged particle energy loss holds information regarding the number of atoms by which the emitted particle passed, thus indicating its original depth. The purpose of this work is to check the capability of Artificial Neural Networks (ANNs) in predicting the boron concentration profile across a boro-silicate sample of thickness 3.5 μm divided into 10 layers. Each layer included different boron concentration than the other. Also, the boron concentration had the values {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. Training, validation, and test data were generated synthetically using MCNP6 in which the boron concentrations varied in the layer number from one sample to another. MCNP6 model consisted of a silicon barrier detector, boro-silicate sample, chamber body and an idealized thermal neutron source. The detector, sample, and the source were located in a voided chamber. The samples were irradiated with a 0.025 eV monoenergetic thermal neutron beam from a monodirectional disk source. To cover the whole area of the samples, the thermal neutron beam had a radius of 3 cm. The silicon detector active volume was modelled as a 100 μm thick and 3 cm radius facing the sample directly. The sample, beam, and the detector were placed on the same axis. Ten ANN regression models were developed, one for each layer boron concentration prediction where the input for each model was the alpha spectrum read by the detector, while the output was the boron concentration for each layer. Results showed regression values higher than 0.94 for all of the developed models. ANNs proved its capability of predicting the boron profile form the alpha spectrum read by the detector regarding neutron depth profiling in a boro-silicate samples.

Highlights

Neutron depth profiling (NDP) is a near-surface analytical technique used to determine the concentration profiles of an impurity isotope as a function of depth. It was first reported by Ziegler et al [1] in 1972 as a near-surface technique which has come to be known as neutron depth profiling, and was later improved by Biersack et al [2] to most of its existing capabilities
It was shown from table I that the total regression score of the ten models varies from 0.94 to 0.99, which illustrates the capability of each model to predict the boron concentration from the corresponding alpha spectra for each layer
The outcome alpha spectrum was obtained from MCNP6 computations

Summary

Introduction

Neutron depth profiling (NDP) is a near-surface analytical technique used to determine the concentration profiles of an impurity isotope as a function of depth (concentration versus depth) It was first reported by Ziegler et al [1] in 1972 as a near-surface technique which has come to be known as neutron depth profiling, and was later improved by Biersack et al [2] to most of its existing capabilities. It is a non-destructive technique based on measuring the energy loss of a charged particle emitted from the tested sample. Analyzing the energy spectrum of the emitted charged particles gives information regarding the number of atoms by which the particle passed while exiting the substrate; indicating its original depth. [3]

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