Forecasting of electrical breakdown in porous silicon using Flicker noise spectroscopy

Parkhutik, V.; Rayón, E.; Ferrer, C.; Тимашев, С. Ф.; Vstovsky, G. V.

doi:10.1002/pssa.200306547

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…Fluctuations of electric current in porous silicon at a fixed anodic voltage were studied by Parkhutik et al [32]. The operating temperature was linearly increased with time from 20 to 250°С, which corresponds to the breakdown conditions.…”

Section: Precursors Of Electric Breakdown In Thin Porous Silicon Filmsmentioning

confidence: 99%

“…FNS has been used to study the dynamics of various processes [26][27][28][29][30][31][32][33][34][35]: voltage fluctuations in electrochemical systems; fluctuations of velocity components in turbulent flows; fluctuation dynamics of solar activity, and so on. It has also been used to parameterize (in terms of "passport" parameters and/or patterns) surface structures studied with scanning probe microscopy, and chaotic segments of infrared or Raman spectra for complex compounds, which are often referred to as the "fingerprint" regions.…”

Section: General Remarksmentioning

confidence: 99%

“…Informativeness of signal irregularities at every level of the spatiotemporal hierarchy of the evolution under study became the basic hypothesis of Flicker-Noise Spectroscopy (FNS) [23][24][25][26][27][28][29][30][31][32][33][34][35][36], a general phenomenological framework for extracting the information stored in various complex signals. This framework allows one to develop algorithms that can be used to determine as many information parameters about the system state based on the analysis of its complex signals as is needed to solve the problems under study.…”

Section: Introductionmentioning

confidence: 99%

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Review of Flicker Noise Spectroscopy in Electrochemistry

Timashev

Polyakov

2007

Fluct. Noise Lett.

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Extraction of information from real signals with chaotically varying components is often a necessary step in the analysis of various physicochemical, electrochemical, and surface phenomena and structures. The 2 potential "interelectrode" correlations in electromembrane systems with overlimiting current densities, as well as to some other problems in electrochemistry, surface science, and medicine are discussed.

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Section: Precursors Of Electric Breakdown In Thin Porous Silicon Filmsmentioning

confidence: 99%

Section: General Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review of Flicker Noise Spectroscopy in Electrochemistry

Timashev

Polyakov

2007

Fluct. Noise Lett.

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“…The FNS is a time-series analysis method that introduces parameters characterizing the components of stochastic signals in different frequency ranges [59]. The method has found numerous applications; among others it is worth mentioning about the use of FNS to the parameterization of images produced by the atomic force microscopy (AFM) [51], analysis of geological signals measured in seismic areas [15, 49], determination of electric breakdowns precursors in thin porous silicon films [39], analysis of electric potential fluctuations in electromembrane systems [58] or monitoring cutting processes and development of stability maps for materials [29]. The FNS method was also successfully applied to some problems in a medical data processing.…”

Section: Introductionmentioning

confidence: 99%

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination

Broniec

2016

Med Biol Eng Comput

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Flicker-noise spectroscopy (FNS) is a general phenomenological approach to analyzing dynamics of complex nonlinear systems by extracting information contained in chaotic signals. The main idea of FNS is to describe an information hidden in correlation links, which are present in the chaotic component of the signal, by a set of parameters. In the paper, FNS is used for the analysis of electroencephalography signal related to the hand movement imagination. The signal has been parametrized in accordance with the FNS method, and significant changes in the FNS parameters have been observed, at the time when the subject imagines the movement. For the right-hand movement imagination, abrupt changes (visible as a peak) of the parameters, calculated for the data recorded from the left hemisphere, appear at the time corresponding to the initial moment of the imagination. In contrary, for the left-hand movement imagination, the meaningful changes in the parameters are observed for the data recorded from the right hemisphere. As the motor cortex is activated mainly contralaterally to the hand, the analysis of the FNS parameters allows to distinguish between the imagination of the right- and left-hand movement. This opens its potential application in the brain–computer interface.

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“…The FNS nonstationarity factor was successfully used for finding precursors of earthquakes [1, 4 5] and electric breakdowns in semiconductor systems [6].…”

Section: Fns Nonstationarity Factormentioning

confidence: 99%

Analysis of Antarctic Ice Core Data (EPICA Dome C) with Flicker-Noise Spectroscopy

2009

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Flicker Noise Spectroscopy (FNS) is a signal analysis toolset for information extraction from stochastic time or space series, which include both regular and chaotic components, based on the analysis of the correlation links for signal irregularities and regular components. The basic FNS tools to extract and analyze the information are power spectra and difference moments (structural functions) of various orders [1].• Peaks in the FNS nonstationarity factor for deuterium composition built at T = 40ka provide a simple and accurate way of identifying all the glacial terminations except for VI, which implies that the transitions are accompanied by major system changes the precursors of which may be located from the analysis of other climate data series.• The absence of any peaks for glacial termination VI indicates that there are no major changes for that transition, which may be associated with some geophysical processes specific to that glacial termination.• There is no peak in the dust flux nonstationarity factor for glacial termination III.• There are no precursors in the nonstationarity factors for both dust composition and dust flux. At the same time, the peaks imply that there should be some preliminary buildup in other dynamic variables, as is the case with earthquakes.• Power spectrum estimates for nonstationarity factors show that there are other important frequencies in addition to the traditional ones associated with orbital parameters, which play a significant role in climate transitions.• The results of this study demonstrate the potential of FNS in the analysis of climate data series and may be used in refining climate transition models. EPICA Dome C Ice Core DataThe FNS nonstationarity factor is applied to the analysis of the EPICA Dome C ice core data for deuterium composition and dust concentration flux recorded for the past 800,000 years, which includes eight climate cycles [7]. The main purpose of the study is to locate and analyze the glacial terminations and frequencies that determine the climate transitions. The ice core data were converted into time series with a sampling interval of 500 years. The time series were then sorted chronologically so that precursors could be searched for. Figure 1. EPICA Dome C ice core data for deuterium (temperature proxy) and dust flux. FNS Nonstationarity Factors for Deuterium and Dust Flux-625500 -625500 -626000 -629486 -626073 VII

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Forecasting of electrical breakdown in porous silicon using Flicker noise spectroscopy

Cited by 9 publications

References 2 publications

Review of Flicker Noise Spectroscopy in Electrochemistry

Review of Flicker Noise Spectroscopy in Electrochemistry

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination

Analysis of Antarctic Ice Core Data (EPICA Dome C) with Flicker-Noise Spectroscopy

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