Dynamics of Spin Relaxation near the Edge of Two-Dimensional Electron Gas

We report calculations of spin relaxation dynamics of two-dimensional electron gas with spin-orbit interaction at the edge region. It is found that the relaxation of spin polarization near the edge is more slow than relaxation in the bulk. That results finally in the spin accumulation at the edge. Time dependence of spin polarization density is calculated analytically and numerically. The mechanism of slower spin relaxation near the edge is related to electrons reflections from the boundary and the lack of the translation symmetry. These reflections partially compensate electron spin precession generated by spin-orbit interaction, consequently making the spin polarization near the edge long living. This effect is accompanied by spin polarization oscillations and spin polarization transfer from the perpendicular to in-plane component

Spin Photovoltaic Effect in Quantum Wires with Rashba Interaction

We propose a mechanism for spin polarized photocurrent generation in quantum wires. The effect is due to the combined effect of Rashba spin-orbit interaction, external magnetic field and microwave radiation. The time-independent interactions in the wire give rise to a spectrum asymmetry in k-space. The microwave radiation induces transitions between spin-splitted subbands, and, due to the peculiar energy dispersion relation, charge and spin currents are generated at zero bias voltage. We demonstrate that the generation of pure spin currents is possible under an appropriate choice of external control parameters

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

SPICE model of memristive devices with threshold

Although memristive devices with threshold voltages are the norm rather than the exception in experimentally realizable systems, their SPICE programming is not yet common. Here, we show how to implement such systems in the SPICE environment. Specifically, we present SPICE models of a popular voltage-controlled memristive system specified by five different parameters for PSPICE and NGSPICE circuit simulators. We expect this implementation to find widespread use in circuits design and testing

Memory effects in complex materials and nanoscale systems

Memory effects are ubiquitous in nature and are particularly relevant at the nanoscale where the dynamical properties of electrons and ions strongly depend on the history of the system, at least within certain time scales. We review here the memory properties of various materials and systems which appear most strikingly in their non-trivial time-dependent resistive, capacitative and inductive characteristics. We describe these characteristics within the framework of memristors, memcapacitors and meminductors, namely memory circuit elements whose properties depend on the history and state of the system. We examine basic issues related to such systems and critically report on both theoretical and experimental progress in understanding their functionalities. We also discuss possible applications of memory effects in various areas of science and technology ranging from digital to analog electronics, biologically-inspired circuits, and learning. We finally discuss future research opportunities in the field.Comment: Review submitted to Advances in Physic