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POSA

Pseudospin-based Antiferromagnetic Magnonics
Funder: European CommissionProject code: 101171325 Call for proposal: ERC-2024-COG
Funded under: HE | ERC | HORIZON-ERC Overall Budget: 1,999,690 EURFunder Contribution: 1,999,690 EUR
Description

Spin-based information processing is a viable alternative to charge-based approaches, enabling low-power devices. In magnetically ordered systems, spin information can be transported via the quantized excitations of the magnetic lattice, called magnons. In an antiferromagnetic system, we generally find two degenerate magnon modes with opposite Néel precession chirality, i.e., opposite spin. These two modes can couple and give rise to complex dynamics and superposition states. I could show that these dynamics can be well described via the pseudospin and its dynamics. My group showed experimentally that electrical pure spin current injection and detection in hematite thin films gives access to the coherent pseudospin dynamics and leads to the manifestation of the magnon Hanle effect. I propose to investigate the coherent spin dynamics induced by pure spin currents in antiferromagnetic insulators, which open up new avenues for energy-efficient information processing beyond von Neumann architectures. The project aims to achieve three main objectives: 1) Establish an experimental platform for pseudospin-based antiferromagnetic magnonics with efficient generation, manipulation, and detection of pseudospin states utilizing antiferromagnetic insulators. This provides the potential for a paradigm shift in magnonics away from wave-based towards spin-based information processing and encoding. 2) Realize pure spin current-driven spin-torque oscillators in antiferromagnetic insulators. This enables the on-chip generation of frequency combs in the microwave and terahertz regime and coherent magnon generation. 3) Explore spiking artificial neurons based on antiferromagnetic spin-torque oscillators. This provides a pathway towards energy-efficient magnonic neural networks and reservoir computing. The successful project will have a transformative effect for energy-efficient information processing, on-chip generation of frequency combs, and hardware for artificial neural networks.

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