Related papers: Distinct memory properties in spin-wave reservoir …
Skyrmions are small topologically protected magnetic structures that hold promise for applications from data storage to neuromorphic computing and they have been shown to possess internal microwave frequency excitations. Skyrmions in a…
Reservoir Computing is a type of recursive neural network commonly used for recognizing and predicting spatio-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The Reservoir…
Emerging resistive-crossbar memory (RCM) technology can be promising for computationally-expensive analog pattern-matching tasks. However, the use of CMOS analog-circuits with RCM would result in large power-consumption and poor…
Reservoir computing (RC) can efficiently process time-series data by transferring the input signal to randomly connected recurrent neural networks (RNNs), which are referred to as a reservoir. The high-dimensional representation of…
Magnetic fluids are excellent candidates for important research fields including energy harvesting, biomedical applications, soft robotics and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that…
Ferromagnets are key materials for sensing and memory applications. In contrast, antiferromagnets that represent the more common form of magnetically ordered materials, have so far found less practical application beyond their use for…
Using micromagnetics we demonstrate that the r.f. field produced by a spin valve can be used to reverse the magnetization in a magnetic nanoparticle. The r.f. field is generated using a current that specifically excites a uniform spin wave…
Reservoir computers (RCs) provide a computationally efficient alternative to deep learning while also offering a framework for incorporating brain-inspired computational principles. By using an internal neural network with random, fixed…
Spintronic devices, such as non-volatile magnetic random access memories and logic devices, have attracted considerable attention as potential candidates for future high efficient data storage and computing technology. In a heavy metal or…
Artificial spin ice (ASI) represents a class of uniquely structured superlattices comprising geometrically arranged interacting nanomagnets. This arrangement facilitates the exploration of magnetic frustration phenomena and related spin…
The increasing popularity of machine learning solutions puts increasing restrictions on this field if it is to penetrate more aspects of life. In particular, energy efficiency and speed of operation is crucial, inter alia in portable…
Developing the field of neuromorphic quantum computing necessitates designing scalable quantum memory devices. Here, we propose a superconducting quantum memory device in the microwave regime, termed as a microwave quantum memcapacitor. It…
A quantum memory at microwave frequencies, able to store the state of multiple superconducting qubits for long times, is a key element for quantum information processing. Electronic and nuclear spins are natural candidates for the storage…
Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting features from the input signal and mapping them into higher dimensional spaces. Physical reservoir layers have been realized…
As a test of general applicability, we use the recently proposed spin-wave delay line active-ring reservoir computer to perform the spoken digit recognition task. On this, classification accuracies of up to 93% are achieved. The tested…
Superradiance is a phenomenon of multiple facets that occurs in classical and quantum physics under extreme conditions. Here we present its manifestation in spin waves under an easily realized condition. We show that an interface between a…
The rectifying effect of radio-frequency (RF) current is highly sensitive in terms of the spatial spin distribution and dynamics. It emerged that an additional spin wave mode was stimulated by the direct-current (DC) current and that this…
Antiferromagnets offer remarkable promise for future spintronics devices, where antiferromagnetic order is exploited to encode information. The control and understanding of antiferromagnetic domain walls (DWs) - the interfaces between…
We investigate spin dynamics in nanodisc arrays of synthetic-antiferromagnets (SAF) made of Py/NiCu/Py trilayers, where the NiCu spacer undergoes a Curie transition at about 200 K. The observed ferromagnetic resonance spectra have three…
Resistive random-access memory (RRAM) is gaining popularity due to its ability to offer computing within the memory and its non-volatile nature. The unique properties of RRAM, such as binary switching, multi-state switching, and device…