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Domain walls (DWs) in magnetic nanowires are promising candidates for a variety of applications including Boolean/unconventional logic, memories, in-memory computing as well as magnetic sensors and biomagnetic implementations. They show rich physical behaviour and are controllable using a number of methods including magnetic fields, charge and spin currents and spin-orbit torques. In this review, we detail types of DWs in ferromagnetic nanowires and describe processes of manipulating their state. We look at the state of the art of DW applications and give our take on the their current status, technological feasibility and challenges. [ABSTRACT FROM AUTHOR]

The conventional computing method based on the von Neumann architecture is limited by a series of problems such as high energy consumption, finite data exchange bandwidth between processors and storage media, etc., and it is difficult to achieve higher computing efficiency. A more efficient unconventional computing architecture is urgently needed to overcome these problems. Neuromorphic computing and stochastic computing have been considered to be two competitive candidates for unconventional computing, due to their extraordinary potential for energy-efficient and high-performance computing. Although conventional electronic devices can mimic the topology of the human brain, these require high power consumption and large area. Spintronic devices represented by magnetic tunnel junctions (MTJs) exhibit remarkable high-energy efficiency, non-volatility, and similarity to biological nervous systems, making them one of the promising candidates for unconventional computing. In this work, we review the fundamentals of MTJs as well as the development of MTJ-based neurons, synapses, and probabilistic-bit. In the section on neuromorphic computing, we review a variety of neural networks composed of MTJ-based neurons and synapses, including multilayer perceptrons, convolutional neural networks, recurrent neural networks, and spiking neural networks, which are the closest to the biological neural system. In the section on stochastic computing, we review the applications of MTJ-based p-bits, including Boltzmann machines, Ising machines, and Bayesian networks. Furthermore, the challenges to developing these novel technologies are briefly discussed at the end of each section. [ABSTRACT FROM AUTHOR]

Composed of computation units each containing a parallel, dual-gate (DG) thin-film transistor (TFT) and a memory capacitor addressed by an access TFT, a neuromorphic circuit implementing fundamental AND and OR logic functions is realized and characterized. The exceptionally low leakage current of a TFT built on indium-gallium-zinc oxide (IGZO) allows the capacitor to be used as a quasi-static memory element. Appropriate weight signals for the realization of the two logic functions are obtained using a gradient-descent training algorithm. The circuit can be configured to execute one of the two functions by the setting of the weight signals. [ABSTRACT FROM AUTHOR]

Biomimicry at the hardware level is expected to overcome at least some of the challenges, including high power consumption, large footprint, two‐dimensionality, and limited functionality, which arise as the field of artificial intelligence matures. One of the main attributes that allow biological systems to thrive is the successful interpretation of and response to environmental signals. Taking inspiration from these systems, the first demonstration of using multiple environmental inputs to trigger the formation and control the growth of an evolvable synaptic transistor is reported here. The resulting transistor exhibits long‐term changes in the channel conductance at a fixed gate voltage. Biomimetic logic circuits are investigated based on this evolvable transistor that implement temperature and pressure inputs to achieve higher order processes like self‐regulation of synaptic strength and coincidence detection. [ABSTRACT FROM AUTHOR]

Due to their nonvolatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of any external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient and the third uses shape variations of the DW track. [ABSTRACT FROM AUTHOR]

The experimental characterization of a network of three resistively coupled microelectromechanical systems (MEMS)-based Colpitts oscillators is presented. The oscillators were fabricated on a printed circuit board and comprised of a 16 MHz quartz resonator in a positive feedback loop, which generated a sustained limit cycle oscillation when powered by a DC voltage supply. It was demonstrated that by varying the coupling strength and uncoupled frequency distribution between the oscillators, frequency synchronization and phase relationships could be controlled. This was examined in isolated pairs of oscillators and in cases where the entire network was interacting. Coupling was achieved with discrete resistors that could be autonomously interchanged with digitally controlled switches. These results build upon a small body of experimental studies by examining a coupling architecture wherein impedance interactions between the oscillators made amplitude dynamics relevant. This is a fundamental step toward fully understanding patterns and behavior in practical networks of coupled MEMS oscillators. The potential for the network to be used in neuromorphic computing and pattern recognition applications is also discussed. [ABSTRACT FROM AUTHOR]