Spin-transfer-driven nano-oscillators are equivalent to parametric resonators

The equivalence between different physical systems permits us to transfer knowledge between them and to characterize the universal nature of their dynamics. We demonstrate that a nanopillar driven by a spin-transfer torque is equivalent to a rotating magnetic plate, which permits us to consider the nanopillar as a macroscopic system under a time-modulated injection of energy, that is, a simple parametric resonator. This equivalence allows us to characterize the phases diagram and to predict magnetic states and dynamical behaviors, such as solitons, stationary textures, and oscillatory localized states, among others. Numerical simulations confirm these predictions.Comment: 8 pages, 7 figure

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Dimerized phase and transitions in a spatially anisotropic square lattice antiferromagnet

We investigate the spatially anisotropic square lattice quantum antiferromagnet. The model describes isotropic spin-1/2 Heisenberg chains (exchange constant J) coupled antiferromagnetically in the transverse (J_\perp) and diagonal (J_\times), with respect to the chain, directions. Classically, the model admits two ordered ground states -- with antiferromagnetic and ferromagnetic inter-chain spin correlations -- separated by a first order phase transition at J_\perp=2J_\times. We show that in the quantum model this transition splits into two, revealing an intermediate quantum-disordered columnar dimer phase, both in two dimensions and in a simpler two-leg ladder version. We describe quantum-critical points separating this spontaneously dimerized phase from classical ones.Comment: 4 pages, 2 figure

Memristor-based Random Access Memory: The delayed switching effect could revolutionize memory design

Memristor’s on/off resistance can naturally store binary bits for non-volatile memories. In this work, we found that memristor’s another peculiar feature that the switching takes place with a time delay (we name it “the delayed switching”) can be used to selectively address any desired memory cell in a crossbar array. The analysis shows this is a must-be in a memristor with a piecewise-linear ?-q curve. A “circuit model”-based experiment has verified the delayed switching feature. It is demonstrated that memristors can be packed at least twice as densely as semiconductors, achieving a significant breakthrough in storage density

Canards from Chua's circuit

The aim of this work is to extend Beno\^it's theorem for the generic existence of "canards" solutions in singularly perturbed dynamical systems of dimension three with one fast variable to those of dimension four. Then, it is established that this result can be found according to the Flow Curvature Method. Applications to Chua's cubic model of dimension three and four enable to state the existence of "canards" solutions in such systems.Comment: arXiv admin note: text overlap with arXiv:1408.489

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms.

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems

Crystal field effects on spin pumping

"Spin pumping" is the injection of spin angular momentum by a time-dependent magnetization into an adjacent normal metal proportional to the spin mixing conductance. We study the role of electrostatic interactions in the form of crystal fields on the pumped spin currents generated by insulators with exchange-coupled local moments at the interface to a metal. The crystal field is shown to render the spin currents anisotropic, which implies that the spin mixing conductance of insulator|normal metal bilayers depends on crystal cut and orientation. We interpret the interface "effective field" (imaginary part of the spin mixing conductance) in terms of the coherent motion of the equilibrium spin density induced by proximity in the normal metal.Comment: 8 pages+, 7 figure

Voltage control of interface rare-earth magnetic moments

The large spin orbit interaction in rare earth atoms implies a strong coupling between their charge and spin degrees of freedom. We formulate the coupling between voltage and the local magnetic moments of rare earth atoms with partially filled 4f shell at the interface between an insulator and a metal. The rare earth-mediated torques allow power-efficient control of spintronic devices by electric field-induced ferromagnetic resonance and magnetization switching