arXiv subjects

Yuriy V. Pershin

Publications and source records attributed to Yuriy V. Pershin.

At least 37 records · Page 2Linked to original sources

arXiv2013

Spin noise spectroscopy of quantum dot molecules

We discuss advantages and limitations of the spin noise spectroscopy for characterization of interacting quantum dot systems on specific examples of individual singly and doubly charged quantum dot molecules (QDMs). It is shown that all the relevant parameters of the QDMs including tunneling amplitudes with spin-conserving and spin-non-conserving interactions, decoherence rates, Coulomb repulsions, anisotropic g-factors and the distance between the dots can be determined by measuring properties of the spin noise power spectrum.

cond-mat.mes-hall↗

arXiv2013

Nonequilibrium spin noise spectroscopy

Spin Noise Spectroscopy (SNS) is an experimental approach to obtain correlators of mesoscopic spin fluctuations in time by purely optical means. We explore the information that this technique can provide when it is applied to a weakly non-equilibrium regime when an electric current is driven through a sample by an electric field. We find that the noise power spectrum of conducting electrons experiences a shift, which is proportional to the strength of the spin-orbit coupling for electrons moving along the electric field direction. We propose applications of this effect to measurements of spin orbit coupling anisotropy and separation of spin noise of conducting and localized electrons.

cond-mat.mes-hall↗

arXiv2013

Two-Beam Spin Noise Spectroscopy

We propose a method of two-beam spin noise spectroscopy to test the spin transport at equilibrium via analysis of correlations between time-shifted spin fluctuations at different space locations. This method allows one to determine the strength of spin-orbit interaction and spin relaxation time and separate spin noise of conducting electrons from the background noise of localized electrons. We formulate a theory of two-beam spin noise spectroscopy in semiconductor wires with Bychkov-Rashba spin-orbit interaction taking into account several possible spin relaxation channels and finite size of laser beams. Our theory predicts a peak shift with respect to the Larmor frequency to higher or lower frequencies depending on the strength of spin orbit interaction and distance between the beams. The two-beam spin noise spectroscopy could find applications in experimental studies of semiconductors, emergent materials and many other systems.

cond-mat.mes-hall↗

arXiv2013

Memory models of adaptive behaviour

Adaptive response to a varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. Here, we consider memory models inspired by an intriguing ability of slime molds to both memorize the period of temperature and humidity variations, and anticipate the next variations to come, when appropriately trained. Effective circuit models of such behavior are designed using i) a set of LC-contours with memristive damping, and ii) a single memcapacitive system-based adaptive contour with memristive damping. We consider these two approaches in detail by comparing their results and predictions. Finally, possible biological experiments that would discriminate between the models are discussed. In this work, we also introduce an effective description of certain memory circuit elements.

q-bio.CB↗

arXiv2013

Self-organization and solution of shortest-path optimization problems with memristive networks

We show that memristive networks-namely networks of resistors with memory-can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time non-locality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.

cs.ET↗

arXiv2012

Second and higher harmonics generation with memristive systems

We show that memristive systems can be used very efficiently to generate passively both double and higher frequency harmonics. A technique for maximizing the power conversion efficiency into any given harmonic is developed and applied to a single memristive system and memristive bridge circuits. We find much higher rates of power conversion compared to the standard diode bridge, with the memristive bridge more efficient for second and higher harmonics generation compared to the single memristive system. The memristive bridge circuit optimized for second harmonic generation behaves as a two-quarter-wave rectifier.

cond-mat.mes-hall↗

arXiv2011

Neuromorphic, Digital and Quantum Computation with Memory Circuit Elements

Memory effects are ubiquitous in nature and the class of memory circuit elements - which includes memristors, memcapacitors and meminductors - shows great potential to understand and simulate the associated fundamental physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically-inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss few examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity of synapses, digital and field-programmable quantum computing.

cond-mat.mes-hall↗

arXiv2011

Solving mazes with memristors: a massively-parallel approach

Solving mazes is not just a fun pastime. Mazes are prototype models in graph theory, topology, robotics, traffic optimization, psychology, and in many other areas of science and technology. However, when maze complexity increases their solution becomes cumbersome and very time consuming. Here, we show that a network of memristors - resistors with memory - can solve such a non-trivial problem quite easily. In particular, maze solving by the network of memristors occurs in a massively parallel fashion since all memristors in the network participate simultaneously in the calculation. The result of the calculation is then recorded into the memristors' states, and can be used and/or recovered at a later time. Furthermore, the network of memristors finds all possible solutions in multiple-solution mazes, and sorts out the solution paths according to their length. Our results demonstrate not only the first application of memristive networks to the field of massively-parallel computing, but also a novel algorithm to solve mazes which could find applications in different research fields.

cond-mat.mes-hall↗

arXiv2011

Emulation of floating memcapacitors and meminductors using current conveyors

We suggest circuit realizations of emulators transforming memristive devices into effective floating memcapacitive and meminductive systems. The emulator's circuits are based on second generation current conveyors and involve either four single-output or two dual-output current conveyors. The equations governing the resulting memcapactive and meminductive systems are presented.

physics.ins-det↗

arXiv2010

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.

cond-mat.mes-hall↗

arXiv2010

Radial Spin Helix in Two-Dimensional Electron Systems with Rashba Spin-Orbit Coupling

We suggest a long-lived spin polarization structure, a radial spin helix, and study its relaxation dynamics. For this purpose, starting with a simple and physically clear consideration of spin transport, we derive a system of equations for spin polarization density and find its general solution in the axially symmetric case. It is demonstrated that the radial spin helix of a certain period relaxes slower than homogeneous spin polarization and plain spin helix. Importantly, the spin polarization at the center of the radial spin helix stays almost unchanged at short times. At longer times, when the initial non-exponential relaxation region ends, the relaxation of the radial spin helix occurs with the same time constant as that describing the relaxation of the plain spin helix.

cond-mat.mes-hall↗

arXiv2010

Ionic Memcapacitive Effects in Nanopores

Using molecular dynamics simulations, we show that, when subject to a periodic external electric field, a nanopore in ionic solution acts as a capacitor with memory (memcapacitor) at various frequencies and strengths of the electric field. Most importantly, the hysteresis loop of this memcapacitor shows both negative and diverging capacitance as a function of the voltage. The origin of this effect stems from the slow polarizability of the ionic solution due to the finite mobility of ions in water. We develop a microscopic quantitative model which captures the main features we observe in the simulations and suggest experimental tests of our predictions. We also suggest a possible memory mechanism due to the transport of ions through the nanopore itself, which may be observed at small frequencies. These effects may be important in both DNA sequencing proposals using nanopores and possibly in the dynamics of action potentials in neurons.

cond-mat.soft↗

arXiv2010

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, that is 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.

q-bio.NC↗

arXiv2010

Memristive circuits simulate memcapacitors and meminductors

We suggest electronic circuits with memristors (resistors with memory) that operate as memcapacitors (capacitors with memory) and meminductors (inductors with memory). Using a memristor emulator, the suggested circuits have been built and their operation has been demonstrated, showing a useful and interesting connection between the three memory elements.

physics.ins-det↗

arXiv2010

Practical approach to programmable analog circuits with memristors

We suggest an approach to use memristors (resistors with memory) in programmable analog circuits. Our idea consists in a circuit design in which low voltages are applied to memristors during their operation as analog circuit elements and high voltages are used to program the memristor's states. This way, as it was demonstrated in recent experiments, the state of memristors does not essentially change during analog mode operation. As an example of our approach, we have built several programmable analog circuits demonstrating memristor-based programming of threshold, gain and frequency.

physics.ins-det↗

arXiv2009

Memristive model of amoeba's learning

Recently, it was shown that the amoeba-like cell {\it Physarum polycephalum} when exposed to a pattern of periodic environmental changes learns and adapts its behavior in anticipation of the next stimulus to come. Here we show that such behavior can be mapped into the response of a simple electronic circuit consisting of an $LC$ contour and a memory-resistor (a memristor) to a train of voltage pulses that mimic environment changes. We also identify a possible biological origin of the memristive behavior in the cell. These biological memory features are likely to occur in other unicellular as well as multicellular organisms, albeit in different forms. Therefore, the above memristive circuit model, which has learning properties, is useful to better understand the origins of primitive intelligence.

q-bio.CB↗

arXiv2009

Effect of Noise on DNA Sequencing via Transverse Electronic Transport

Previous theoretical studies have shown that measuring the transverse current across DNA strands while they translocate through a nanopore or channel may provide a statistically distinguishable signature of the DNA bases, and may thus allow for rapid DNA sequencing. However, fluctuations of the environment, such as ionic and DNA motion, introduce important scattering processes that may affect the viability of this approach to sequencing. To understand this issue, we have analyzed a simple model that captures the role of this complex environment in electronic dephasing and its ability to remove charge carriers from current-carrying states. We find that these effects do not strongly influence the current distributions due to the off-resonant nature of tunneling through the nucleotides - a result we expect to be a common feature of transport in molecular junctions. In particular, only large scattering strengths, as compared to the energetic gap between the molecular states and the Fermi level, significantly alter the form of the current distributions. Since this gap itself is quite large, the current distributions remain protected from this type of noise, further supporting the possibility of using transverse electronic transport measurements for DNA sequencing.

cond-mat.soft↗

arXiv2009

Circuit elements with memory: memristors, memcapacitors and meminductors

We extend the notion of memristive systems to capacitive and inductive elements, namely capacitors and inductors whose properties depend on the state and history of the system. All these elements show pinched hysteretic loops in the two constitutive variables that define them: current-voltage for the memristor, charge-voltage for the memcapacitor, and current-flux for the meminductor. We argue that these devices are common at the nanoscale where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales. These elements and their combination in circuits open up new functionalities in electronics and they are likely to find applications in neuromorphic devices to simulate learning, adaptive and spontaneous behavior.

cond-mat.mes-hall↗