Related papers: Magnonic Full Adder Based on 2D Chiral Magnonic Re…
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…
Dipolar coupling between closely spaced magnetic waveguides enables the design of magnonic directional couplers - universal devices capable of functioning as signal combiners, power splitters, demultiplexers, and more. The…
In contrast to lossy plasmonic metasurfaces (MSs), wideband dielectric MSs comprising of subwavelength nanostructures supporting Mie resonances are of great interest in the visible wavelength range. Here, for the first time to our…
We present a method of calculation of the effective magnetic permeability of magnonic metamaterials containing periodically arranged magnetic inclusions of arbitrary shapes. The spectrum of spin wave modes confined in the inclusions is…
We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data. Modern machine learning methods require large, robust training data sets to generate accurate predictions.…
In this work, we present experimental data demonstrating the possibility of using magnonic holographic devices for pattern recognition. The prototype eight-terminal device consists of a magnetic matrix with micro-antennas placed on the…
Forecasting complex, chaotic signals is a central challenge across science and technology, with implications ranging from secure communications to climate modeling. Here we demonstrate that magnons - the collective spin excitations in…
Spintronic nano-synapses and nano-neurons perform complex cognitive computations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial…
A diode, a device allowing unidirectional signal transmission, is a fundamental element of logic structures and lies in the heart of modern information systems. Spin wave or magnon, representing a collective quasi-particle excitation of the…
Recently memristor-based applications and circuits are receiving an increased attention. Furthermore, memristors are also applied in logic circuit design. Material implication logic is one of the main areas with memristors. In this paper an…
This work reports the design, fabrication, and characterization of coupling-enhanced magnetostatic forward volume wave resonators with significant spur suppression. The fabrication is based on surface micro-machining of yttrium iron garnet…
We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural…
Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this…
Microresonator devices which posses ultra-high quality factors are essential for fundamental investigations and applications. Microsphere and microtoroid resonators support remarkably high Q's at optical frequencies, while planarity…
Spintronic devices are considered as promising candidates in implementing neuromorphic systems or hardware neural networks, which are expected to perform better than other existing computing systems for certain data classification and…
The excitation and detection of propagating spin waves with lithographed nanoantennas underpin both classical magnonic circuits and emerging quantum technologies. Here, we establish a framework for all-electrical propagating spin-wave…
Chiral lattice modes are hybrid states arising from chiral plasmonic particles assembled in ordered arrays with opportune periodicity. These resonances exhibit dependence on excitation handedness, and their observation in plasmonic lattices…
Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role…
In the last decades, collinear magnetic insulating systems have emerged as promising energy-saving information carriers. Their elementary collective spin excitations, i.e., magnons, can propagate for long distances bypassing the Joule…
Realizing novel topological states in magnonic systems unlocks robust, low-power spin-wave devices. In this letter, we show that incorporating left-handed spin waves (antimagnons) fundamentally reorganizes band topology, and enables tunable…