2007/01/29 by C. Pennetta, Pennetta, C., Eleonora Alfinito +4
Computer Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Applications #Statistical Mechanics (cond-mat.stat-mech) #Theoretical and Computational Physics #cond-mat.mtrl-sci #cond-mat.stat-mech
paper · pdf · doi:10.48550/arxiv.cond-mat/0701712
10 pages, 13 figures, submitted to Phys. Rev. E
arxiv created 2007/01/29 · openalex publication_date 2007/01/29 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
We introduce a multi-species network model which describes the resistance fluctuations of a resistor in a non-equilibrium stationary state. More precisely, a thin resistor characterized by a 1/falpha resistance noise is described as a two-dimensional network made by different species of elementary resistors. The resistor species are distinguished by their resistances and by their energies associated with thermally activated processes of breaking and recovery. Depending on the external conditions, stationary states of the network can arise as a result of the competition between these processes. The properties of the network are studied as a function of the temperature by Monte Carlo simulations carried out in the temperature range 300 ÷800 K. At low temperatures, the resistance fluctuations display long-term correlations expressed by a power-law behavior of the auto-correlation function and by a value approx 1 of the alpha-exponent of the spectral density. On the contrary, at high temperatures the resistance fluctuations exhibit a finite and progressively smaller correlation time associated with a non-exponential decay of correlations and with a value of the alpha-exponent smaller than one. This temperature dependence of the alpha coefficient reproduces qualitatively well the experimental findings.