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A method of RTS noise identification in noise signals of semiconductor devices in the time domain

Stawarz-Graczyk B., Dokupil D., Flisikowski P.

Metrology and Measurement Systems

2010

Vol. 17, nr 1

95-107

In the paper a new method of Random Telegraph Signal (RTS) noise identification is presented. The method is based on a standardized histogram of instantaneous noise values and processing by Gram-Charlier series. To find a device generating RTS noise by the presented method one should count the number of significant coefficients of the Gram-Charlier series. This would allow to recognize the type of noise. There is always one (first) significant coefficient (c0) representing Gaussian noise. If additional coefficients cr (where r > 0) appear it means that RTS noise (two-level as well as multiple-level) is detected. The coefficient representing the Gaussian component always has the highest value of all. The application of this method will be presented on the example of four devices, each with different noise (pure Gaussian noise signal, noise signal with two-level RTS noise, noise signal with three-level RTS noise and noise signal with not precisely visible occurrence of RTS noise).

Some properties of polynomial-normal distributions associated with hermite polynomials

Plucińska A.

Demonstratio Mathematica

1999

Vol. 32, nr 1

195-206

This paper considers a class of densities formed by taking the prod uct of nonnegative polynomials and normal densities. We investigate some relations of these densities with Hermite polylnomials. We construct a set of polynomials orthogonal with respect to the polynomial-normal density (PND ) . We invesigate the distribution of sums of independent random variables (r.v.) with PND. We construct a stochastic process such that the one-dimensional density of this process is PND.