Publication

Design and optimization of nanogap array sensors

Jürgen Brugger, Luis Guillermo Villanueva Torrijo, Thomas Kiefer
2008
Posters

Abstract

We present a percolation based model which is suitable for the design of sensors based on highly ordered nanogap arrays. Monte-Carlo simulations and analytical expressions are used to calculate the sensitivity and dynamic range as a function of the topology and geometry of the arrays, as well as the statistical distribution of the thresholds for conduction across the nanogaps.

Official source

https://infoscience.epfl.ch/entities/publication/e304ba2b-8696-483f-bd8b-30f386495b61

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Jürgen Brugger, Luis Guillermo Villanueva Torrijo, Thomas Kiefer
2008
Posters

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e304ba2b-8696-483f-bd8b-30f386495b61

About this result

Related concepts (23)

Statistical model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process. When referring specifically to probabilities, the corresponding term is probabilistic model. A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables.

Empirical distribution function

In statistics, an empirical distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical measure of a sample. This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified value of the measured variable is the fraction of observations of the measured variable that are less than or equal to the specified value.

Statistical assumption

Statistics, like all mathematical disciplines, does not infer valid conclusions from nothing. Inferring interesting conclusions about real statistical populations almost always requires some background assumptions. Those assumptions must be made carefully, because incorrect assumptions can generate wildly inaccurate conclusions. Here are some examples of statistical assumptions: Independence of observations from each other (this assumption is an especially common error). Independence of observational error from potential confounding effects.

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