Reliable signal processing using parallel arrays of non-identical nanostructures and stochastic resonance

In the stochastic resonance (SR) phenomena, the response of a non-linear system to a weak periodic input signal is optimised by the presence of a particular level of noise which enhances signal detection. We explore, theoretically, the influence of thermal noise in arrays of metal nanoparticles functionalised with organic ligands acting as tunnelling junctions, with emphasis on the interplay between the SR phenomena and the nanostructure variability. In this system, the transference of a reduced number of electrons may suffice to implement a variety of electronic functions. However, because nanostructures are expected to show a significant variability in their physical characteristics, it is important to study the relation between the diversity-induced static noise and the dynamic noise caused by thermal fluctuations. We consider an ideal model based on the Coulomb blockade and tunnelling effects that includes the stochastic nature of electron transference due to thermal noise together with the nanostructure variability found in experimental distribution functions. The correlation between the input (potential) and the output (current) signals, as well as the absolute value of the current and its time fluctuations, are analysed as a function of the temperature and the number of nanostructures. Extensive kinetic Monte Carlo simulations suggest that the interplay between thermal noise and variability could permit reliable processing of weak signals with many non-identical nanostructures.