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- W2010367613 abstract "Abstract Three advanced algorithms for the analysis of ion beam data are presented. With simulated annealing the inverse ion beam analysis problem (from the data, determine the depth profile) is solved in an automatic way. With Bayesian inference using the Markov chain Monte‐Carlo method one can extract all the information present in the data, including confidence limits for the depth profile of each element. Finally, with artificial neural networks, selected parameters of interest in specific systems can be extracted in an instantaneous way. The basics of the three algorithms are given and their advantages and disadvantages are discussed. Three examples of application are shown. In the first two, the three algorithms are used in succession to illustrate the capabilities of each one. We show that complex data can be analysed with ease and accuracy. Rapidly changing non‐Rutherford cross‐sections do not pose a problem to analysis using these algorithms. Roughness information can be extracted from multilayered samples. In general, the use of Bayesian inference, simulated annealing or an artificial neural network is a trade‐off between degree of automation, accuracy and efficiency of analysis. In the last example, an artificial neural network is applied to the analysis of RBS data of AlNO thin films on Si. Copyright © 2003 John Wiley & Sons, Ltd." @default.
- W2010367613 created "2016-06-24" @default.
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- W2010367613 date "2003-09-01" @default.
- W2010367613 modified "2023-09-23" @default.
- W2010367613 title "Advanced data analysis techniques for ion beam analysis" @default.
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- W2010367613 doi "https://doi.org/10.1002/sia.1599" @default.
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