Related papers: Perspective on Nanoscaled Magnonic Networks
Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for…
This paper studies numerically how the signal detector arrangement influences the performance of reservoir computing using spin waves excited in a ferrimagnetic garnet film. This investigation is essentially important since the input…
Inverse design, the process of matching a device or process parameters to exhibit a desired performance, is applied in many disciplines ranging from material design over chemical processes and to engineering. Machine learning has emerged as…
Neural networks have recently gained attention in solving inverse problems. One prominent methodology are Physics-Informed Neural Networks (PINNs) which can solve both forward and inverse problems. In the paper at hand, full waveform…
The outlook for producing useful practical devices within the paradigm of magnonics rests on our ability to emit, control and detect coherent exchange spin waves on the nanoscale. Here, we argue that all these key functionalities can be…
The key enabling factor for Spin Wave (SW) technology utilization for building ultra low power circuits is the ability to energy efficiently cascade SW basic computation blocks. SW Majority gates, which constitute a universal gate set for…
Spin waves are excitations in ferromagnetic media that have been proposed as information carriers in hybrid spintronic devices with much lower operation power than conventional charge-based electronics. Their wave nature can be exploited in…
Engineered quantum systems enabling novel capabilities for communication, computation, and sensing have blossomed in the last decade. Architectures benefiting from combining distinct and complementary physical quantum systems have emerged…
A new concept in magnonics studies the dynamics of spin waves (SWs) in three-dimensional nanosystems. It is a natural evolution from conventionally used planar systems to explore magnetization configurations and dynamics in 3D…
There is an urgent need to enhance the storage density of memory devices to accommodate the exponentially increasing amount of data generated by humankind. In this work, we describe Magnonic Combinatorial Memory (MCM), where the bits of…
Neurons in the brain behave as non-linear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behavior to realize high density, low power neuromorphic computing will require huge…
The unprecedented advancement of artificial intelligence has placed immense demands on computing hardware, but traditional silicon-based semiconductor technologies are approaching their physical and economic limit, prompting the exploration…
We investigated a possible use of the magnonic interferometric switches in multi-valued logic circuits. The switch is a three-terminal device consisting of two spin channels where input, control, and output signals are spin waves. Signal…
Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks.…
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…
Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by electrically manipulating the collective dynamics of the electron…
Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from…
One recent breakthrough in the field of magnonics is the experimental realization of reconfigurable spin-wave nanochannels formed by magnetic domain wall with a width of $10-100$ nm [Wagner \emph{et al}., Nat. Nano. \textbf{11}, 432…
Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications,…
Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be…