Application of genetic algorithms for characterization of thin layered materials by glancing incidence X-ray reflectometry

Dane, A.D.; Veldhuis, A.; Boer, Dick K. G. de; Leenaers, A.; Buydens, L.M.C.

doi:10.1016/s0921-4526(98)00398-6

Cited by 66 publications

(44 citation statements)

References 5 publications

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“…After GA was used in the characterization of thin layer materials by glancing incidence x-ray reflectometry, 24 a hybrid model of genetic algorithm and ML was studied. 25 The two-step approach was shown to improve significantly estimations in glancing incidence XRF.…”

Section: Application Of Genetic Algorithms To Xrsmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

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Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x-ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least-squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt-Levenberg algorithms, to a certain extent.

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Section: Application Of Genetic Algorithms To Xrsmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

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“…Dane et al [90] applied GAs to the curve fitting required by glancing incidence X-ray reflectometry, obtaining results better than those obtained with a currently used method, reducing the amount of human effort and expertise required for analysing reflectivity measurements, and reducing the probability of overlooking feasible solutions. The same group [91] implemented a two-step fundamental parameter method for model-free analysis of thin-layered materials by X-ray fluorescence spectrometry, in which a gradient technique is used to refine the results of a GA used to obtain the number of layers and, for each layer, an estimate of the elementary concentrations and thickness.…”

Section: Miscellaneousmentioning

confidence: 99%

Genetic algorithms in chemometrics and chemistry: a review

Leardi

2001

Journal of Chemometrics

351

176

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SUMMARYThe use of genetic algorithms has been growing exponentially since Holland published the first papers about them. Thanks to the extraordinary increase in calculation power, nowadays it is possible to apply them to extremely complex problems. A considerable number of papers in which genetic algorithms have been applied have been published in several scientifical journals. This review is of course far from being a complete summary of the application of genetic algorithms to chemical problems; its goal is to show the reader the main fields of application of this technique, together with providing a list of references on the subject.

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“…The obtained individual element XPS profiles at several different annealing temperatures were curve fitted to several annealing temperatures, which showed the composition changes of oxides and the layer structures of the films. Consequently, to analyze the obtained XRR spectra, a 4-layer material model of HfO 2 (low-density)/HfO 2 /HfSi x O y /SiO 2 /Si-substrate based on XPS analysis was constructed to improve fitting process with XRR genetic algorithm (GA) analysis [5]. As a result, the film thickness of the HfO 2 films with modified material structure models were obtained accurately.…”

Section: Introductionmentioning

confidence: 99%

Layer structure variation of thermal-processed thin HfO<inf>2</inf> films using X-ray Reflectivity and X-ray Photoelectron Spectroscopy

Chang

2011

2011 11th IEEE International Conference on Nanotechnology

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As transistor feature sizes scaling down, the ultra-thin HfO 2 high-k dielectric has been used to replace SiO 2 for the gate dielectric for a better EOT. Grazing Incidence X-ray Reflectivity (GIXRR) is an accurate instrument to analyze the ultra-thin HfO 2 films non-destructively based on an appropriate material model. However, the interfaces between layers of the ultra-thin HfO2 and other oxide films are not easily identified, especially when post-deposition annealing (PDA) process is applied. In this work, X-ray Photoelectron Spectroscopy (XPS) was used to evaluate the layer structures which were post-annealed up to 1000 °C using furnaces in the Ar ambiances. The experimental results and analysis showed that layer thicknesses, crystal phases and chemical structures of the ultra-thin HfO 2 films were significantly dependent on annealing temperatures. The structure analysis of the GIXRR spectra using the modified material structure model from the XPS analysis confirmed the interfacial broadening induced by the post-deposition annealing.

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Application of genetic algorithms for characterization of thin layered materials by glancing incidence X-ray reflectometry

Cited by 66 publications

References 5 publications

Chemometrics and its applications to x‐ray spectrometry

Chemometrics and its applications to x‐ray spectrometry

Genetic algorithms in chemometrics and chemistry: a review

Layer structure variation of thermal-processed thin HfO<inf>2</inf> films using X-ray Reflectivity and X-ray Photoelectron Spectroscopy

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