  A short review is presented of a recently developed computational approach
which allows the study of the resistance noise over the full range of bias
values, from the linear regime up to electrical breakdown. Resistance noise is
described in terms of two competing processes in a random resistor network. The
two processes are thermally activated and driven by an electrical bias. In the
linear regime, a scaling relation has been found between the relative variance
of resistance fluctuations and the average resistance. The value of the
critical exponent is significantly higher than that associated with 1/f noise.
In the nonlinear regime, occurring when the bias overcomes the threshold value,
the relative variance of resistance fluctuations scales with the bias. Two
regions can be identified in this regime: a moderate bias region and a
pre-breakdown one. In the first region, the scaling exponent has been found
independent of the values of the model parameters and of the bias conditions. A
strong nonlinearity emerges in the pre-breakdown region which is also
characterized by non-Gaussian noise. The results compare well with measurements
of electrical breakdown in composites and with electromigration experiments in
metallic lines.
