C. Pennetta

Biased percolation and abrupt failure of electronic devices

We propose a new percolation model as an aid to understand abrupt failure of electronic devices. It is called biased percolation because we assume that local Joule heating determines the probability of generating defects causing percolative breakdown of the device. We take as a simple geometry a homogeneous thin film, modelled as a two-dimensional resistor network. By carrying out Monte Carlo simulations we investigate the evolution of the system including: the damage pattern, current distribution, resistance degradation, resistance relative fluctuations and its power spectrum associated with 1/f noise. Our results show that biased percolation efficiently simulates degradation of thin films in good agreement with available experiments and predicts several features that should take place close to the abrupt failure of most devices.

Trapping-detrapping fluctuations in organic space-charge layers

A trapping-detrapping model is proposed for explaining the current fluctuation behavior in organic semiconductors (polyacenes) operating under current-injection conditions. The fraction of ionized traps obtained from the current-voltage characteristics, is related to the relative current noise spectral density at the trap-filling transition. The agreement between theory and experiments validates the model and provides an estimate of the concentration and energy level of deep traps.

A network model to correlate conformational change and the impedance spectrum of single proteins

One of the main trend in to date research and development is the miniaturization of electronic devices. In this perspective, integrated nanodevices based on proteins or biomolecules are attracting a major interest. In fact, it has been shown that proteins like bacteriorhodopsin and azurin, manifest electrical properties which are promising for the development of active components in the field of molecular electronics. Here we focus on two relevant kinds of proteins: The bovine rhodopsin, prototype of GPCR protein, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer disease. Both these proteins exert their functioning starting with a conformational change of their native structure. Our guess is that such a change should be accompanied with a detectable variation of their electrical properties. To investigate this conjecture, we present an impedance network model of proteins, able to estimate the different electrical response associated with the different configurations. The model resolution of the electrical response is found able to monitor the structure and the conformational change of the given protein. In this respect, rhodopsin exhibits a better differential response than AChE. This result gives room to different interpretations of the degree of conformational change and in particular supports a recent hypothesis on the existence of a mixed state already in the native configuration of the protein.

Resistance and Resistance Fluctuations in Random Resistor Networks Under Biased Percolation

We consider a two-dimensional random resistor network (RRN) in the presence of two competing biased processes consisting of the breaking and recovering of elementary resistors. These two processes are driven by the joint effects of an electrical bias and of the heat exchange with a thermal bath. The electrical bias is set up by applying a constant voltage or, alternatively, a constant current. Monte Carlo simulations are performed to analyze the network evolution in the full range of bias values. Depending on the bias strength, electrical failure or steady state are achieved. Here we investigate the steady state of the RRN focusing on the properties of the non-Ohmic regime. In constant-voltage conditions, a scaling relation is found between /(0) and V/V(0), where is the average network resistance, (0) the linear regime resistance, and V0 the threshold value for the onset of nonlinearity. A similar relation is found in constant-current conditions. The relative variance of resistance fluctuations also exhibits a strong nonlinearity whose properties are investigated. The power spectral density of resistance fluctuations presents a Lorentzian spectrum and the amplitude of fluctuations shows a significant non-Gaussian behavior in the prebreakdown region. These results compare well with electrical breakdown measurements in thin films of composites and of other conducting materials.

Thermal effects on the electrical degradation of thin film resistors

Recently we introduced a biased percolation model to study the electrical failure of thin-film resistors. Here we extend this model by allowing thermal interactions among first neighbour elemental resistances and accounting for the dependence of each elemental resistance on the local temperature. Monte Carlo simulations are performed to investigate the main properties of the film degradation such as: damage pattern, film lifetime, evolution of the resistance and of the 1/f resistance–noise spectrum.

Biased percolation and electrical breakdown

To analyse the degradation of a thin-film conductor we have extended the biased percolation model to the case of electrical breakdown associated with a systematic decrease of the resistance. As relevant indicators of the degradation process we have chosen the damage pattern, the current and temperature distributions, the change of resistance, the lifetime, the relative resistance fluctuations and its power spectrum associated with 1/f noise. Our results are in a satisfactory agreement with available experiments, exhibiting several features which take place close to the abrupt failure of a thin-film device, and confirm the usefulness of the biased percolation model as a tool to investigate degradation processes. Analogies and differences between the two opposite situations when degradation occurs with a systematic increase or decrease of the resistance are discussed.

Biased resistor network model for electromigration failure and related phenomena in metallic lines

Electromigration phenomena in metallic lines are studied by using a biased resistor network model. The void formation induced by the electron wind is simulated by a stochastic process of resistor breaking, while the growth of mechanical stress inside the line is described by an antagonist process of recovery of the broken resistors. The model accounts for the existence of temperature gradients due to current crowding and Joule heating. Alloying effects are also accounted for. Monte Carlo simulations allow the study within a unified theoretical framework of a variety of relevant features related to the electromigration. The predictions of the model are in excellent agreement with the experiments and in particular with the degradation towards electrical breakdown of stressed

Distribution of return intervals of extreme events

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Non-Gaussian resistance noise near electrical breakdown in granular materials

thin metallic lines. Detailed investigations refer to the damage pattern, the distribution of the times to failure (TTFs), the generalized Blacks law, the time evolution of the resistance, including the early-stage change due to alloying effects and the electromigration saturation appearing at low current densities or for short line lengths. The dependence of the TTFs on the length and width of the metallic line is also well reproduced. Finally, the model successfully describes the resistance noise properties under steady state conditions.

A percolative approach to electromigration in metallic lines

Abstract.The distribution of return intervals of extreme events is studied in time series characterized by finite-term correlations with non-exponential decay. Precisely, it has been analyzed the statistics of the return intervals of extreme values of the resistance fluctuations displayed by resistors with granular structure in nonequilibrium stationary states. The resistance fluctuations are calculated by Monte Carlo simulations using a resistor network approach. It has been found that for highly disordered networks, when the auto-correlation function displays a non-exponential and non-power-law decay, the distribution of return intervals of the extreme values is a stretched exponential, with exponent independent of the threshold.