Solid-state memcapacitive system with negative and diverging capacitance

We suggest a possible realization of a solid-state memory capacitive (memcapacitive) system. Our approach relies on the slow polarization rate of a medium between plates of a regular capacitor. To achieve this goal, we consider a multi-layer structure embedded in a capacitor. The multi-layer structure is formed by metallic layers separated by an insulator so that non-linear electronic transport (tunneling) between the layers can occur. The suggested memcapacitor shows hysteretic charge-voltage and capacitance-voltage curves, and both negative and diverging capacitance within certain ranges of the field. This proposal can be easily realized experimentally, and indicates the possibility of information storage in memcapacitive devices

Thermoelectric phenomena in disordered open quantum systems

Using a stochastic quantum approach, we study thermoelectric transport phenomena at low temperatures in disordered electrical systems connected to external baths. We discuss three different models of one-dimensional disordered electrons, namely the Anderson model of random on-site energies, the random-dimer model and the random-hopping model - also relevant for random-spin models. We find that although the asymptotic behavior of transport in open systems is closely related to that in closed systems for these noninteracting models, the magnitude of thermoelectric transport strongly depends on the boundary conditions and the baths spectral properties. This shows the importance of employing theories of open quantum systems in the study of energy transport.Comment: 5 pages, 2 figures, revised versio

Role of heating and current-induced forces in the stability of atomic wires

We investigate the role of local heating and forces on ions in the stability of current-carrying aluminum wires. We find that heating increases with wire length due to a red shift of the frequency spectrum. Nevertheless, the local temperature of the wire is relatively low for a wide range of biases provided good thermal contact exists between the wire and the bulk electrodes. On the contrary, current-induced forces increase substantially as a function of bias and reach bond-breaking values at about 1 V. These results suggest that local heating promotes low-bias instabilities if dissipation into the bulk electrodes is not efficient, while current-induced forces are mainly responsible for the wire break-up at large biases. We compare these results to experimental observations.Comment: 4 pages, 4 figure

Experimental demonstration of associative memory with memristive neural networks

When someone mentions the name of a known person we immediately recall her face and possibly many other traits. This is because we possess the so-called associative memory - the ability to correlate different memories to the same fact or event. Associative memory is such a fundamental and encompassing human ability (and not just human) that the network of neurons in our brain must perform it quite easily. The question is then whether electronic neural networks - electronic schemes that act somewhat similarly to human brains - can be built to perform this type of function. Although the field of neural networks has developed for many years, a key element, namely the synapses between adjacent neurons, has been lacking a satisfactory electronic representation. The reason for this is that a passive circuit element able to reproduce the synapse behaviour needs to remember its past dynamical history, store a continuous set of states, and be "plastic" according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behaviour with electronic neural networks