Related papers: A Ferroelectric Tunnel Junction-based Integrate-an…
Synaptic memory consolidation has been heralded as one of the key mechanisms for supporting continual learning in neuromorphic Artificial Intelligence (AI) systems. Here we report that a Fowler-Nordheim (FN) quantum-tunneling device can…
Due to the limitations of realizing artificial neural networks on prevalent von Neumann architectures, recent studies have presented neuromorphic systems based on spiking neural networks (SNNs) to reduce power and computational cost.…
We study dynamics of tunnel Josephson junctions with a thin ferromagnetic layer F [superconductor-insulator-ferromagnet-superconductor (SIFS) junctions]. On the basis of derived equations relating the superconducting phase and magnetic…
Limitations in processing capabilities and memory of today's computers make spiking neuron-based (human) whole-brain simulations inevitably characterized by a compromise between bio-plausibility and computational cost. It translates into…
Neuromorphic computing aims to replicate the brain's remarkable energy efficiency and parallel processing capabilities for large-scale artificial intelligence applications. In this work, we present a comprehensive comparative study of three…
Stochastic magnetic tunnel junctions (sMTJ) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, sMTJs…
Superparamagnetic tunnel junctions are nanostructures that auto-oscillate stochastically under the effect of thermal noise. Recent works showed that despite their stochasticity, such junctions possess a capability to synchronize to…
Multiferroic tunnel junctions (MFTJs) have aroused significant interest due to their functional properties useful for non-volatile memory devices. So far, however, all the existing MFTJs have been based on perovskite-oxide heterostructures…
We report a complementary metal oxide semiconductor (CMOS) technology compatible ferroelectric tunnel junction memristor grown directly on top of a Silicon substrate using a scandium doped aluminum nitride as the ferroelectric layer.
With the ever-increasing energy need to process big data, the realization of low-power computing technologies, such as superconducting logic and memories, has become a pressing issue. Developing fast and non-volatile superconducting memory…
Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to…
We report the fabrication and testing, at 4.2 K, of an SISFS device, where S, F, and I denote a superconductor (Nb), a ferromagnetic material (permalloy), and an insulator (AlOx), respectively. The F layer covers about one half of the top…
Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in…
Hardware neural networks could perform certain computational tasks orders of magnitude more energy-efficiently than conventional computers. Artificial neurons are a key component of these networks and are currently implemented with…
Multilayer edge molecular spintronics device (MEMSD) approach can produce novel logic and memory units for the computers. MEMSD are produced by bridging the molecular channels across the insulator, in the exposed edge region(s) of a…
With the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently emerged as possible solutions. In…
In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based…
Spintronic nano-neurons offer a promising route towards energy-efficient, high-performance hardware neural networks thanks to their inherent low-input nonlinear dynamics. However, training such networks remains a major bottleneck as it…
Magnetic tunnel junctions (MTJs), composed of two ferromagnetic electrodes separated by a thin insulating barrier layer, are currently used in spintronic devices, such as magnetic sensors and magnetic random access memories. Recently,…
We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and…