2014
DOI: 10.1016/j.ndteint.2013.11.007
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Ultrasonic characterization of porous silicon using a genetic algorithm to solve the inverse problem

Abstract: International audienceThis paper presents a method for ultrasonic characterization of porous silicon in which a genetic algorithm based optimization is used to solve the inverse problem. A one dimensional model describing wave propagation through a water immersed sample is used in order to compute transmission spectra. Then, a water immersion wide bandwidth measurement is performed using insertion/substitution method and the spectrum of signals transmitted through the sample is calculated using Fast Fourier Tr… Show more

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Cited by 33 publications
(18 citation statements)
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“…These however require measurements of bulk waves in different crystallographic directions 27 , which evidently is problematic for thin porous membranes. Therefore, these methods generally only reported values for the longitudinal bulk wave velocity in the thickness direction 28,29 . Furthermore, most of these methods typically use a coupling medium between the transducers and the sample under investigation.…”
mentioning
confidence: 99%
“…These however require measurements of bulk waves in different crystallographic directions 27 , which evidently is problematic for thin porous membranes. Therefore, these methods generally only reported values for the longitudinal bulk wave velocity in the thickness direction 28,29 . Furthermore, most of these methods typically use a coupling medium between the transducers and the sample under investigation.…”
mentioning
confidence: 99%
“…The numerical results confirm that this method produced convergent and stable numerical solutions [15]. Bustillo et al presented a method for ultrasonic characterization of porous silicon in which a genetic algorithm-based optimization is used to solve the inverse problem [16]. Capasso et al solved inverse problems of differential equations by a "generalized collage" method and application to a mean field stochastic model [17].…”
Section: Introductionmentioning
confidence: 71%
“…Then, Goldberg developed a floating-point based genetic algorithms. This optimization method is currently used in several research fields [3], [10], [11]. In this study, we have chosen a fitness function based on the least-square distance between theoretical and experimental curves.…”
Section: B Genetic Algorithmmentioning
confidence: 99%
“…For simplicity, the inverse of distance is used. In order to limit premature convergence risk, a parameter is added to the distance to limit the maximal value of fitness function (5) [10]. This parameter is set at 1, enabling a good convergence of this optimization problem [10].…”
Section: B Genetic Algorithmmentioning
confidence: 99%
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