Related papers: Magnetic-field controlled organic spintronic memri…
Spintronic neurons which emit sharp voltage spikes are required for the realization of hardware neural networks enabling fast data processing with low-power consumption. In many neuroscience and computer science models, neurons are…
Memristive devices whose resistance can be hysteretically switched by electric field or current are intensely pursued both for fundamental interest as well as potential applications in neuromorphic computing and phase-change memory. When…
Modern computers perform pre-defined operations using static memory components, whereas biological systems learn through inherently dynamic, time-dependent processes in synapses and neurons. The biological learning process also relies on…
Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where a network trained on one task forgets the solution when…
In this work, we simulate the functionality of artificial neuron and synapse using spin-orbit torque-based spintronic devices and implemented a fully connected artificial neural netwrok (ANN). These neuro-synaptic devices are emulated using…
Spin-orbitronics, based on both spin and orbital angular momentum, presents a promising pathway for energy-efficient memory and logic devices. Recent studies have demonstrated the emergence of orbital currents in light transition metals…
Non-volatile resistive switching is demonstrated in memristors with nanocrystalline molybdenum disulfide (MoS$_2$) as the active material. The vertical heterostructures consist of silicon, vertically aligned MoS$_2$ and chrome / gold metal…
Altermagnets represent a novel magnetic phase with transformative potential for ultrafast spintronics, yet efficient control of their magnetic states remains challenging. We demonstrate an ultra-low-power electric-field control of…
Neuromorphic networks of artificial neurons and synapses can solve computational hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, silicon neuromorphic processors outperform graphic…
Electrophysiological techniques have improved substantially over the past years to the point that neuroprosthetics applications are becoming viable. This evolution has been fuelled by the advancement of implantable microelectrode…
Spin-Hall nano-oscillators (SHNO) are nanoscale spintronic devices that generate high-frequency (GHz) microwave signals useful for various applications such as neuromorphic computing and creating Ising systems. Recent research demonstrated…
An impressive success of spintronic applications has been typically realized in metal-based structures which utilize magnetoresistive effects for substantial improvements in the performance of computer hard drives and magnetic random access…
Diffusive memristors owing to their ability to produce current spiking when a constant or slowly changing voltage is applied are competitive candidates for the development of artificial electronic neurons. These artificial neurons can be…
The memristors are expected to be fundamental devices for neuromorphic systems and switching applications. For example, the device made of a sandwiched layer of poly(N-vinylcarbazole) and reduced graphene composite between asymmetric…
Memristors are an electronic device whose resistance depends on the voltage history that has been applied to its two terminals. Despite its clear advantage as a computational element, a suitable transport model is lacking for the special…
Memristor (resistor with memory), inductor with memory (meminductor) and capacitor with memory (memcapacitor) have different roles to play in novel computing architectures. We found that a coil with a magnetic core is an inductor with…
Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence. Deep learning is based on computational models that are, to a certain extent, bio-inspired, as…
Learning with physical systems is an emerging paradigm that seeks to harness the intrinsic nonlinear dynamics of physical substrates for learning. The impetus for a paradigm shift in how hardware is used for computational intelligence stems…
Macroscopic spin ensembles possess brain-like features such as non-linearity, plasticity, stochasticity, selfoscillations, and memory effects, and therefore offer opportunities for neuromorphic computing by spintronics devices. Here we…
Continued progress in high speed computing depends on breakthroughs in both materials synthesis and device architectures. The performance of logic and memory can be enhanced significantly by introducing a memristor, a two terminal device…