The straintronic spin-neuron

Pure voltage-driven spintronic neuron based on stochastic magnetization switching behaviour

Voltage-driven stochastic magnetization switching in a nanomagnet has attracted more attention recently with its superiority in achieving energy-efficient artificial neuron. Here, a novel pure voltage-driven scheme with ∼27.66 aJ energy dissipation is proposed, which could rotate magnetization vector randomly using only a pair of electrodes covered on the multiferroic nanomagnet. Results show that the probability of 180° magnetization switching is examined as a sigmoid-like function of the voltage pulse width and magnitude, which can be utilized as the activation function of designed neuron. Considering the size errors of designed neuron in fabrication, it's found that reasonable thickness and width variations cause little effect on recognition accuracy for MNIST hand-written dataset. In other words, the designed pure voltage-driven spintronic neuron could tolerate size errors. These results open a new way toward the realization of artificial neural network with low power consumption and high reliability.

Applications of nanomagnets as dynamical systems: I

When magnets are fashioned into nanoscale elements, they exhibit a wide variety of phenomena replete with rich physics and the lure of tantalizing applications. In this topical review, we discuss some of these phenomena, especially those that have come to light recently, and highlight their potential applications. We emphasize what drives a phenomenon, what undergirds the dynamics of the system that exhibits the phenomenon, how the dynamics can be manipulated, and what specific features can be harnessed for technological advances. For the sake of balance, we point out both advantages and shortcomings of nanomagnet based devices and systems predicated on the phenomena we discuss. Where possible, we chart out paths for future investigations that can shed new light on an intriguing phenomenon and/or facilitate both traditional and non-traditional applications.

Emerging Artificial Neuron Devices for Probabilistic Computing

In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.

Differentiated Strain-Control of Localized Magnetic Modes in Antidot Arrays

The control of localized magnetic modes has been obtained in Ni₆₀Fe₄₀ square lattice (600 nm) antidot arrays. This has been performed by tailoring the magnetoelastic field at the scale of the antidot primitive cell. The corresponding heterogeneous strain field distributions have been generated by a PZT substrate and enhanced by the incorporation of a supporting compliant layer. It has been highlighted by a differentiated variation of magnetic energy directly due to the local magnetoelastic field felt by each magnetic mode, probed by ferromagnetic resonance spectroscopy. A modeling, involving micromagnetic simulations (to locate the magnetic modes), full-field simulations (to evaluate the strain field distributions), and an analytical model generally dedicated to continuous film that we have extended to those magnetic modes, shows a good agreement with the experimental data. This approach is very promising to develop multichannel systems with simultaneous and differentiated controlled frequencies in magnetic devices.

Tight-binding Hamiltonian considering up to the third nearest neighbours for trans polyacetylene

Utilizing the linear combination of atomic orbitals in the Slater-Koster approach in combination with the density functional theory band structure data, a new tight-binding Hamiltonian up to the third nearest neighbours for the dimerized trans polyacetylene is proposed. The effects of strain are also considered in the Hamiltonian by varying the distance between two successive CH groups along the molecular symmetry axis. Using this new Hamiltonian and exploiting the Green's function method in the framework of the Landauer-Büttiker formalism, the electronic transport properties in a trans polyacetylene chain in the presence and absence of strain are studied. It is shown that at a peculiar value of compression strain, the electron conductance shifts 0.27 eV in energy which is an exploitable magnitude for straintronic applications of the trans polyacetylene specially as strain sensors and strain switches.

Energy-efficient switching of nanomagnets for computing: straintronics and other methodologies

The need for increasingly powerful computing hardware has spawned many ideas stipulating, primarily, the replacement of traditional transistors with alternate 'switches' that dissipate miniscule amounts of energy when they switch and provide additional functionality that are beneficial for information processing. An interesting idea that has emerged recently is the notion of using two-phase (piezoelectric/magnetostrictive) multiferroic nanomagnets with bistable (or multi-stable) magnetization states to encode digital information (bits), and switching the magnetization between these states with small voltages (that strain the nanomagnets) to carry out digital information processing. The switching delay is ∼1 ns and the energy dissipated in the switching operation can be few to tens of aJ, which is comparable to, or smaller than, the energy dissipated in switching a modern-day transistor. Unlike a transistor, a nanomagnet is 'non-volatile', so a nanomagnetic processing unit can store the result of a computation locally without refresh cycles, thereby allowing it to double as both logic and memory. These dual-role elements promise new, robust, energy-efficient, high-speed computing and signal processing architectures (usually non-Boolean and often non-von-Neumann) that can be more powerful, architecturally superior (fewer circuit elements needed to implement a given function) and sometimes faster than their traditional transistor-based counterparts. This topical review covers the important advances in computing and information processing with nanomagnets, with emphasis on strain-switched multiferroic nanomagnets acting as non-volatile and energy-efficient switches-a field known as 'straintronics'. It also outlines key challenges in straintronics.