An Overview on Statistical Modeling of Random Telegraph Noise in the Frequency Domain

Statistical supervised learning with engineering data: a case study of low frequency noise measured on semiconductor devices

“…We have considered a sample of n = 100 subjects, each one being tested at J = 3 different experimental conditions. The experiment has been repeated a total of R = 500 times and the results are summarized in Table 4, from where it can be assessed the good performance of the method, in particular for the estimation of the parametric component of the model, as it is deduced for the low estimated values of bias (5) and standard deviation (6), which have been obtained, respectively as follows:…”

“…Noise is a stochastic signal and unpredictable, therefore it is necessary to use statistical methods to analyze its behavior. Any tentative to understand the random behavior of the underlying phenomenon can allow to undertake further improvements, see [4,5] and [6], among others. One interesting investigation line is to analyze the problem from a statistical perspective and in this sense the study of potential correlations between the switching threshold voltage and noise characteristics is of great importance, as considered in [7], and yet it has received little attention in the current literature.…”

“…Although the literature on stochastic models for explaining the random behavior of low frequency noise is not short (see [4,6,8,9]), however there are not many studies specifically focused on the relationship noise-voltage from a statistical learning point of view, that is, based on data. This is where the statistician can get into the game with the aim of adapting powerful machine learning techniques that have been far demonstrated their efficacy both theoretic and practically and bringing them to this particular application field of Physics.…”

Statistical Supervised Learning With Engineering Data: A Case Study of Low Frequency Noise Measured On Semiconductor Devices

“…Noise is a stochastic signal and unpredictable, therefore it is necessary to use statistical methods to analyze its behavior. Any tentative to understand the random behavior of the underlying phenomenon can allow to undertake further improvements, see [20], [1] and [3], among others. One interesting investigation line is to analyze the problem from a statistical perspective and in this sense the study of potential correlations between the switching threshold voltage and noise characteristics is of great importance, as considered in [7], and yet it has received little attention in the current literature.…”

“…Although the literature on stochastic models for explaining the random behavior of low frequency noise is not short (see [16], [23], [1], [3]), however there are not many studies specifically focused on the relationship noise-voltage from a statistical learning point of view, that is, based on data. This is where the statistician can get into the game with the aim of adapting powerful machine learning techniques that have been far demonstrated their efficacy both theoretic and practically and bringing them to this particular application field of Physics.…”