Machine learning plasma-surface interface for coupling sputtering and gas-phase transport simulations

Thin film processing by means of sputter deposition inherently depends on the interaction of energetic particles with a target surface and the subsequent particle transport. The length and time scales of the underlying physical phenomena span orders of magnitudes. A theoretical description which bridges all time and length scales is not practically possible. Advantage can be taken particularly from the well-separated time scales of the fundamental surface and plasma processes. Initially, surface properties may be calculated from a surface model and stored for a number of representative cases. Subsequently, the surface data may be provided to gas-phase transport simulations via appropriate model interfaces (e.g., analytic expressions or look-up tables) and utilized to define insertion boundary conditions. During run-time evaluation, however, the maintained surface data may prove to be not sufficient. In this case, missing data may be obtained by interpolation (common), extrapolation (inaccurate), or be supplied on-demand by the surface model (computationally inefficient). In this work, a potential alternative is established based on machine learning techniques using artificial neural networks. As a proof of concept, a multilayer perceptron network is trained and verified with sputtered particle distributions obtained from transport of ions in matter based simulations for Ar projectiles bombarding a Ti-Al composite. It is demonstrated that the trained network is able to predict the sputtered particle distributions for unknown, arbitrarily shaped incident ion energy distributions. It is consequently argued that the trained network may be readily used as a machine learning based model interface (e.g., by quasi-continuously sampling the desired sputtered particle distributions from the network), which is sufficiently accurate also in scenarios which have not been previously trained

Measurement of the quenching factor of Na recoils in NaI(Tl)

Measurements of the quenching factor for sodium recoils in a 5 cm diameter NaI(Tl) crystal at room temperature have been made at a dedicated neutron facility at the University of Sheffield. The crystal has been exposed to 2.45 MeV mono-energetic neutrons generated by a Sodern GENIE 16 neutron generator, yielding nuclear recoils of energies between 10 and 100 keVnr. A cylindrical BC501A detector has been used to tag neutrons that scatter off sodium nuclei in the crystal. Cuts on pulse shape and time of flight have been performed on pulses recorded by an Acqiris DC265 digitiser with a 2 ns sampling time. Measured quenching factors of Na nuclei range from 19% to 26% in good agreement with other experiments, and a value of 25.2 \pm 6.4% has been determined for 10 keV sodium recoils. From pulse shape analysis, the mean times of pulses from electron and nuclear recoils have been compared down to 2 keVee. The experimental results are compared to those predicted by Lindhard theory, simulated by the SRIM Monte Carlo code, and a preliminary curve calculated by Prof. Akira Hitachi.Comment: 21 pages, 13 figure

Numerical modeling study of a neutron depth profiling (NDP) system for the Missouri S&T reactor

”For decades, Neutron Depth Profiling has been used for the non-destructive analysis and quantification of boron in electronic materials and lithium in lithium ion batteries. NDP is one of the few non-destructive analytical techniques capable of measuring the depth profiles of light elements to depths of several microns with nanometer spatial resolution. The technique, however, is applicable only to a handful of light elements with large neutron absorption cross sections. This work discusses the possibility of coupling Particle Induced X-ray Emission spectroscopy with Neutron Depth Profiling to yield additional information about the depth profiles of other elements within a material. The technical feasibility of developing such a system at the Missouri University of Science and Technology Reactor (MSTR) beam port is discussed. This work uses a combination of experimental neutron flux measurements with Monte Carlo radiation transport calculations to simulate a proposed NDP-PIXE apparatus at MSTR. In addition, the possibility of implementing an Artificial Neural Network to perform automated data analysis of NDP is presented. It was found that the performance of the Artificial Neural Network is at least as accurate as traditional processing approaches using stopping tables but with the added advantage that the Artificial Neural Network method requires fewer geometric approximations and accounts for all charged particle transport physics implicitly”--Abstract, page iii

Machine learning for the prediction of stopping powers

The stopping power of a material upon interaction with an energetic ion is the key measure of how far that ion will travel. The implications of accurate particle range calculations are tremendous, affecting every single application in which particle radiation is involved, from nuclear power to medicine. An approach is presented which attempts to overcome current shortcomings in the theoretical understanding of stopping power, as well as the methods used to interpret and exploit measured data. This is a considerable challenge, however the use of a novel machine learning methodology is shown to hold great promise in this endeavour: the ultimate aim being the ability to correctly predict the stopping value for any energy, ion and target combination, having no pre-existing experimental data. A random forest regression algorithm is trained using over 34,000 experimental measurements, representing stopping power values for 522 ion-target combinations across the energy range 10-3 to 102 MeV/amu, and ion and target atomic masses 1 to >240. Evaluation is carried out using several fundamental error metrics, over the whole dataset as well as for individual combinations, to provide the most comprehensive understanding of performance when tested under strict cross-validation criteria. The resulting model is shown to yield predicted stopping power curves corresponding closely to those of the true experimental values, with an ability to generalise across target elements, compounds, mixtures, alloys and polymers, irrespective of phase, and for a wide range of ion masses

A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran

Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves

Metal oxides of resistive memories investigated by electron and ion backscattering

The memristor is one of the most promising devices being studied for multiple uses in future electronic systems, with applications ranging from nonvolatile memories to artificial neural networks. Its working is based on the forming and rupturing of nano-scaled conductive filaments, which drastically alters the device’s resistance. These filaments are formed by oxygen vacancy accumulation, hence a deep understanding of the self-diffusion of oxygen in these systems is necessary. Accurate measurements of oxygen self-diffusion on metal oxides was achieved with the development of a quantitative analysis of the energy spectrum of the backscattering of electrons. The novel technique called Electron Rutherford Backscattering Spectroscopy (ERBS) uses the scattering of high energy electrons ( 40 keV) to probe the sample’s near surface (10–100 nm). Measurements of the high energy loss region – called Reflection High-Energy Electron Loss Spectroscopy (RHEELS) – also exhibit characteristics of the material’s electronic structure. A careful procedure was developed for the fitting of ERBS spectra, which was then applied on the analysis of multi-layered samples of Si3N4/TiO2, and measurements of the band gap of common oxides, such as SiO2, CaCO3 and Li2CO3. Monte Carlo simulations were employed to study the effects of multiple elastic scatterings in ERBS spectra, and a dielectric function description of inelastic scatterings extended the simulation to also consider the plasmon excitation peaks observed in RHEELS. These analysis tools were integrated into a package named PowerInteraction. With its use, a series of measurements of oxygen self-diffusion in TiO2 were conducted. The samples were composed of two sputtered deposited TiO2 layers, one of which was enriched with the 18 mass oxygen isotope. After thermal annealing, diffusion profiles were obtained by tracking the relative concentration of oxygen isotopes in both films. From the logarithmic temperature dependence of the diffusion coefficients, an activation energy of 1.05 eV for oxygen self-diffusion in TiO2 was obtained. Common ion beam analysis, such as RBS and NRA/NRP (Nuclear Reaction Analysis/Profiling), were also used to provide complementary information.O memristor é um dos dispositivos mais promissores sendo estudados para múltiplos usos em sistemas eletrônicos, com aplicações desde memórias não voláteis a redes neurais artificiais. Seu funcionamento é baseado na formação e ruptura de filamentos condutores nanométricos, o que altera drasticamente a resistência do dispositivo. Estes filamentos são formados pela acumulação de vacâncias de oxigênio, portanto um profundo entendimento da autodifusão de oxigênio nestes sistemas é necessário. Medidas acuradas da difusão em óxidos metálicos foi obtida com o desenvolvimento de uma análise quantitativa do espectro em energia de elétrons retroespalhados. A inovadora técnica de RBS de elétrons (ERBS) utiliza elétrons de alta energia ( 40 keV) para investigar a região próxima a superfície (10–100 nm). Medidas da região de alta perda de energia – chamada de Spectroscopia de Perda de Alta-Energia de Elétrons Refletidos (RHEELS) – também exibe características da estrutura eletrônica dos materiais. Um procedimento cuidadoso para o ajuste de espectros de ERBS foi desenvolvido, e então aplicado na análise de amostras multi camada de Si3N4/TiO2, e medidas de band gap de alguns óxidos, como SiO2, CaCO3 e Li2CO3. Simulações de Monte Carlo foram empregadas no estudo dos efeitos de espalhamento múltiplo nos espectros de ERBS, e uma descrição dielétrica dos espalhamentos inelásticos extendeu as simulação para também considerarem os picos de exitação plasmônica observados em RHEELS. Estas ferramentas de análise foram integradas em um pacote chamado PowerInteraction. Com o uso deste, uma série de medidas de autodifusão de oxigênio em TiO2 foram conduzidas. As amostras eram compostas por dois filmes de TiO2 depositados por sputtering, um dos quais enriquecido com isótopo 18 de oxigênio. Após tratamentos térmicos, perfis de difusão foram obtidos pelo rastreio das concentrações relativas dos isótopos de oxigênio nos dois filmes. Do comportamento logarítmico dos coeficientes de difusão em relação à temperatura, uma energia de ativação de 1.05 eV para a autodifusão de oxigênio em TiO2 foi obtida. Análises por feixes de íons, como RBS e NRA/NRP (Análise/Perfilometria por Reação Nuclear), também forneceram informações complementares

Análisis de la primera toma de datos subterránea y estudios de fondo del experimento Argon Dark Matter

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física Atómica, Molecular y Nuclear, leída el 03/06/2016Dark Matter is a collisionless and non-luminous kind of matter, whose existence is inferred through its gravitational effects at the galactic, cluster and large scales in the Universe. From the analysis of the PLANCK latest data, Dark Matter accounts for the 26.6% of the composition of the energy density of the Universe, while ordinary matter only represents 4.9% [1]. Revealing the nature of Dark Matter has become one of the most challenging problems in modern physics. A possible explanation comes from particle physics in the form of Weakly Interacting Massive Particles (WIMPs) , a particularly interesting class of new particle that can naturally account for the measured abundance of Dark Matter. WIMPs would be produced thermally in the early Universe and, since they interact only weakly, their annihilation rate would become insignificant as the Universe expands, thus freezing out with a relic abundance. Supersymmetry, an extension of the Standard Model of particle physics, foresees interesting possible WIMP candidates in the form of the Lightest Supersymmetric Particle ( LSP) , which is neutral, stable and massive. A great experimental effort has been undertaken in the last years to detect Dark Matter in underground and space-based detectors or produce it in accelerators. This Thesis is focused on the analysis of the first underground run and background studies of the Argon Dark Matter (ArDM) experiment, which aims to detect WIMPs via the nuclear recoils produced by their elastic scattering off argon nuclei. The detector is a ton-scale double-phase (liquid-gas) TPC, which is currently installed at the Canfranc Underground Laboratory ( LSC) under the Pyrenees in Spain and it is the first ton-scale argon detector for Dark Matter to take data underground...La Materia Oscura es un tipo ele materia no luminosa y sin colisiones cuya existencia es inferida a través de sus efectos gravitacionales en la escala galáctica, de cúmulos de galaxias y a grandes escalas en el Universo. De acuerdo con el análisis de los datos más recientes de PLANCK, la Materia Oscura da cuenta del 26,6% de la composición de la densidad de energía del Universo, mientras que la materia ordinaria sólo representa un 4,9% [1]. Revelar la naturaleza de la Materia Oscura se ha convertido en uno de los problemas más desafiantes de la física moderna. Una posible explicación proviene de la física de partículas en la forma de Partículas Masivas Débilmente Interactuantes (WIMPs), una nueva clase de partícula especialmente interesante que puede dar cuenta de forma natural de la abundancia medida de Materia Oscura. Los WIMPs se producirían térmicamente en el Universo primitivo y, puesto que sólo interaccionan débilmente, su tasa de aniquilación se convertiría en insignificante conforme el Universo se expande, permaneciendo así con una abundancia determinada. Supersimetría, una extensión del Modelo Estándar de partículas, proporciona candidatos a WIMPs en forma de la Partícula Supersimétrica Más Ligera (LSP), que es neutra, estable y masiva. Un gran esfuerzo experimental ha sido realizado en los últimos años para detectar Materia Oscura con detectores subterráneos o espaciales o para producirla en aceleradores. Esta Tesis está centrada en el análisis de la primera toma de datos subterránea y estudios de fondo del experimento Argon Dark Matter (ArDM), cuyo objetivo es detectar WIMPs a través de los retrocesos nucleares producidos por su dispersión elástica por núcleos de argón. El detector, actualmente instalado en el Laboratorio Subterráneo de Canfranc (LSC) bajo los Pirineos en España, es una TPC de doble fase (líquido-gas) de una tonelada y es el primer detector de Materia Oscura basado en argón de la escala de la tonelada en tomar elatos en un laboratorio subterráneo...Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEunpu

Active radiation detectors for use in space beyond low earth orbit: spatial and energy resolution requirements and methods for heavy ion charge classification

2017 Summer.Includes bibliographical references.Space radiation exposure to astronauts will need to be carefully monitored on future missions beyond low earth orbit. NASA has proposed an updated radiation risk framework that takes into account a significant amount of radiobiological and heavy ion track structure information. These models require active radiation detection systems to measure the energy and ion charge Z. However, current radiation detection systems cannot meet these demands. The aim of this study was to investigate several topics that will help next generation detection systems meet the NASA objectives. Specifically, this work investigates the required spatial resolution to avoid coincident events in a detector, the effects of energy straggling and conversion of dose from silicon to water, and methods for ion identification (Z) using machine learning. The main results of this dissertation are as follows: 1. Spatial resolution on the order of 0.1 cm is required for active space radiation detectors to have high confidence in identifying individual particles, i.e., to eliminate coincident events. 2. Energy resolution of a detector system will be limited by energy straggling effects and the conversion of dose in silicon to dose in biological tissue (water). 3. Machine learning methods show strong promise for identification of ion charge (Z) with simple detector designs

Source Apportionment and Forecasting of Aerosol in a Steel City - Case Study of Rourkela

Urban air pollution is one of the biggest problems ascending due to rapid urbanization and industrialization. The improvement of air quality in an urban area in general, constitutes of three phases, monitoring, modeling and control measures. The present research work addresses the requirements of the urban air quality management programme (UAQMP) in Rourkela steel city. A typical UAQMP contains three aspects: monitoring of air pollution, modeling of air pollution and taking control measures. The present study aims to conduct the modeling of particulate air pollution for a steel city. Modeling of particulate matter (PM) pollution is nothing but the application of different mathematical models in source apportionment and forecasting of PM. PM (PM10 and TSP) was collected twice a week for two years (2011-2012) during working hours in Rourkela. The seasonal variations study of PM showed that the aerosol concentration was high during summer and low during monsoon. A detailed chemical characterization of both PM10 and TSP was carried out to find out the concentrations of different metal ions, anions and carbon content. The Spearman rank correlation analysis between different chemical species of PM depicted the presence of both crustal and anthropogenic origins in particulate matter. The enrichment factor analysis highlighted the presence of anthropogenic sources. Three major receptor models were used for the source apportionment of PM, namely chemical mass balance model (CMB), principal component analysis (PCA) and positive matrix factorization (PMF). In selecting source profiles for CMB, an effort has been put to select the profiles which represent the local conditions. Two of the profiles, namely soil dust and road dust, were developed in the present study for better accuracy. All three receptor models have shown that industrial (40-45%) and combustion sources (30-35%) were major contributors to particulate pollution in Rourkela. Artificial neural networks (ANN) were used for the prediction of particulate pollution using meteorological parameters as inputs. The emphasis is to compare the performances of MLP and RBF algorithms in forecasting and provide a rigorous inter-comparison as a first step toward operational PM forecasting models. The training, testing and validation errors of MLP networks are significantly lower than that of RBF networks. The results indicate that both MLP and RBF have shown good prediction capabilities while MLP networks were better than that of RBF networks. There is no profound bias that can be seen in the models which may also suggest that there are very few or zero external factors that may influence the dispersion and distribution of particulate matter in the study area