Resistance noise in Bi_2Sr_2CaCu_2O8+δ_{8+\delta}

The resistance noise in a Bi_2Sr_2CaCu_2O$_{8+\delta}$ thin film is found to increase strongly in the underdoped regime. While the increase of the raw resistance noise with decreasing temperature appears to roughly track the previously reported pseudogap temperature for this material, standard noise analysis rather suggests that the additional noise contribution is driven by the proximity of the superconductor-insulator transition

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Tuning the Correlation Decay in the Resistance Fluctuations of Multi-Species Networks

A new network model is proposed to describe the $1/f^\alpha$ resistance noise in disordered materials for a wide range of $\alpha$ values ($0< \alpha < 2$). More precisely, we have considered the resistance fluctuations of a thin resistor with granular structure in different stationary states: from nearly equilibrium up to far from equilibrium conditions. This system has been modelled as a network made by different species of resistors, distinguished by their resistances, temperature coefficients and by the energies associated with thermally activated processes of breaking and recovery. The correlation behavior of the resistance fluctuations is analyzed as a function of the temperature and applied current, in both the frequency and time domains. For the noise frequency exponent, the model provides $0< \alpha < 1$ at low currents, in the Ohmic regime, with $\alpha$ decreasing inversely with the temperature, and $1< \alpha <2$ at high currents, in the non-Ohmic regime. Since the threshold current associated with the onset of nonlinearity also depends on the temperature, the proposed model qualitatively accounts for the complicate behavior of $\alpha$ versus temperature and current observed in many experiments. Correspondingly, in the time domain, the auto-correlation function of the resistance fluctuations displays a variety of behaviors which are tuned by the external conditions.Comment: 26 pages, 16 figures, Submitted to JSTAT - Special issue SigmaPhi200

Low-frequency Current Fluctuations in Individual Semiconducting Single-Wall Carbon Nanotubes

We present a systematic study on low-frequency current fluctuations of nano-devices consisting of one single semiconducting nanotube, which exhibit significant 1/f-type noise. By examining devices with different switching mechanisms, carrier types (electrons vs. holes), and channel lengths, we show that the 1/f fluctuation level in semiconducting nanotubes is correlated to the total number of transport carriers present in the system. However, the 1/f noise level per carrier is not larger than that of most bulk conventional semiconductors, e.g. Si. The pronounced noise level observed in nanotube devices simply reflects on the small number of carriers involved in transport. These results not only provide the basis to quantify the noise behavior in a one-dimensional transport system, but also suggest a valuable way to characterize low-dimensional nanostructures based on the 1/f fluctuation phenomenon

Long-range potential fluctuations and 1/f noise in hydrogenated amorphous silicon

We present a microscopic theory of the low-frequency voltage noise (known as "1/f" noise) in micrometer-thick films of hydrogenated amorphous silicon. This theory traces the noise back to the long-range fluctuations of the Coulomb potential produced by deep defects, thereby predicting the absolute noise intensity as a function of the distribution of defect activation energies. The predictions of this theory are in very good agreement with our own experiments in terms of both the absolute intensity and the temperature dependence of the noise spectra.Comment: 8 pages, 3 figures, several new parts and one new figure are added, but no conceptual revision

Strong Suppression of Electrical Noise in Bilayer Graphene Nano Devices

Low-frequency 1/f noise is ubiquitous, and dominates the signal-to-noise performance in nanodevices. Here we investigate the noise characteristics of single-layer and bilayer graphene nano-devices, and uncover an unexpected 1/f noise behavior for bilayer devices. Graphene is a single layer of graphite, where carbon atoms form a 2D honeycomb lattice. Despite the similar composition, bilayer graphene (two graphene monolayers stacked in the natural graphite order) is a distinct 2D system with a different band structure and electrical properties. In graphene monolayers, the 1/f noise is found to follow Hooge's empirical relation with a noise parameter comparable to that of bulk semiconductors. However, this 1/f noise is strongly suppressed in bilayer graphene devices, and exhibits an unusual dependence on the carrier density, different from most other materials. The unexpected noise behavior in graphene bilayers is associated with its unique band structure that varies with the charge distribution among the two layers, resulting in an effective screening of potential fluctuations due to external impurity charges. The findings here point to exciting opportunities for graphene bilayers in low-noise applications

Point process model of 1/f noise versus a sum of Lorentzians

We present a simple point process model of $1/f^{\beta}$ noise, covering different values of the exponent $\beta$. The signal of the model consists of pulses or events. The interpulse, interevent, interarrival, recurrence or waiting times of the signal are described by the general Langevin equation with the multiplicative noise and stochastically diffuse in some interval resulting in the power-law distribution. Our model is free from the requirement of a wide distribution of relaxation times and from the power-law forms of the pulses. It contains only one relaxation rate and yields $1/f^ {\beta}$ spectra in a wide range of frequency. We obtain explicit expressions for the power spectra and present numerical illustrations of the model. Further we analyze the relation of the point process model of $1/f$ noise with the Bernamont-Surdin-McWhorter model, representing the signals as a sum of the uncorrelated components. We show that the point process model is complementary to the model based on the sum of signals with a wide-range distribution of the relaxation times. In contrast to the Gaussian distribution of the signal intensity of the sum of the uncorrelated components, the point process exhibits asymptotically a power-law distribution of the signal intensity. The developed multiplicative point process model of $1/f^{\beta}$ noise may be used for modeling and analysis of stochastic processes in different systems with the power-law distribution of the intensity of pulsing signals.Comment: 23 pages, 10 figures, to be published in Phys. Rev.