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MANNGA

Financiado

MAGNONIC ARTIFICIAL NEURAL NETWORKS AND GATE ARRAYS

We seek to explore and challenge the limits of spin-based devices and their energy efficiency. This will be achieved by combining two inherently energy-efficient technology paradigms: (i) magnonics (using spin waves – low energy m... ver más

31/08/2025

AALTO

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

AALTO KORKEAKOULUSAATIO SR No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 6 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-06-17

HORIZON-CL4-2021-DIGITAL-EMERGING-01-14: Advanced spintronics: Unleashing sp... Expected Outcome:Proposal results are expected to contribute to the following expected outcomes:

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Cerrada hace 3 años

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6 Participantes

AALTO

713.63K€ | Lider

UNEXE

N.D. | Participante

INNOVENT EV

426.06K€ | Participante

MLU

690.71K€ | Participante

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Duración del proyecto: 38 meses Fecha Inicio: 2022-06-17
Fecha Fin: 2025-08-31

Líder del proyecto

AALTO KORKEAKOULUSAATIO SR No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto We seek to explore and challenge the limits of spin-based devices and their energy efficiency. This will be achieved by combining two inherently energy-efficient technology paradigms: (i) magnonics (using spin waves – low energy magnetic excitations – to process signals and data) and (ii) neuromorphic computing (using large-scale integrated systems and analog circuits to solve data-driven problems in a brain-like manner). We will use nanoscale chiral magnonic resonators as building blocks of artificial neural networks. The power of the networks will be demonstrated by creating magnonics versions of field programmable gate arrays, reservoir computers, and recurrent neural networks. The ultimate efficiency of the devices will be achieved by (a) maximising their magnetic nonlinearity; (b) using epitaxial yttrium iron garnet, which has the lowest known magnetic damping, for thin film magnonic media and resonators; and (c) using wireless delivery of power. Sensitive to the resonators’ micromagnetic states, such artificial neural networks will be conveniently programmable and trainable within existing paradigms of magnetic data storage. The latter includes magnetic random-access memory (MRAM), which is already compatible with CMOS, while compatibility with other technology paradigms of spintronics will also be sought, explored, and exploited. Thereby, the key ambition of our proposed very forward-looking research programme is to develop and establish a novel, revolutionary class of energy-efficient spin-based components and devices for use in green high-tech data communication, processing, and storage technologies, thereby helping unlock the full potential of spintronics. We will seek dissemination of our developed and appropriately protected designs, processes, and technologies to interested European and international companies, thereby improving the competitiveness of the European high-tech industry.

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