Flexible Antiferromagnetic FeRh Tapes as Memory Elements

Fina, Ignasi; Dix, N.; Menéndez, Enric; Crespi, Anna; Foerster, Michael; Aballe, Lucía; Sánchez, F.; Fontcuberta, Josep

doi:10.1021/acsami.0c00704

Cited by 14 publications

(23 citation statements)

References 56 publications

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“…amplitude of the applied electric field was shown in 2017 by Fina and co-workers 34 . As it has very recently been demonstrated by the same group, FeRh films preserving the temperature-dependent AFM-FM transition can be grown on a flexible metallic substrate coated with a textured MgO layer 35 , a fact making the FeRh/MgO interface especially relevant for large-scale applications.…”

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confidence: 91%

Reversible control of magnetism in FeRh thin films

Merkel

Lengyel

Nagy

et al. 2020

Sci Rep

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The multilayer of approximate structure MgO(100)/[ n fe 51 Rh 49 (63 Å)/ 57 fe 51 Rh 49 (46 Å)] 10 deposited at 200 °C is primarily of paramagnetic A1 phase and is fully converted to the magnetic B2 phase by annealing at 300 °C for 60 min. Subsequent irradiation by 120 keV Ne + ions turns the thin film completely to the paramagnetic A1 phase. Repeated annealing at 300 °C for 60 min results in 100% magnetic B2 phase, i.e. a process that appears to be reversible at least twice. The A1 → B2 transformation takes place without any plane-perpendicular diffusion while Ne + irradiation results in significant interlayer mixing. Besides the traditional "faster-smaller-cheaper" directive, a new requirement has recently arisen for the newly developed devices: the energy efficiency. The electricity consumption of information technology is expected to reach 13% of the global utilization in the next decade 1,2 , of which nearly 50% is due to unwanted heat dissipation. Therefore, the study of novel materials and developing new operation principles for energy-saving applications in information technology are essential for supporting sustainable development. From this point of view, the fine control of magnetism is a great step towards reducing energy consumption of information storage by orders of magnitude 3-11. The Fe-Rh system is an excellent playground for developing energy-efficient magnetic devices 12,13. The equilibrium phase diagram of solid Fe-Rh 14,15 includes a great variety of phases of different magnetic behaviour. The disordered bcc A2 (α or δ, prototype W) phases are, depending on temperature, ferro-or paramagnetic (PM). The ordered bcc B2 (α' or α'' , prototype CsCl) phases show ferro-, antiferro-or paramagnetism at different temperatures and concentrations. The disordered fcc A1 (γ, prototype Cu) phase is PM at all investigated temperatures. The existence of a monoclinic antiferromagnetic (AFM) ground state was predicted by DFT calculations in the equiatomic FeRh alloy in 2016 16. However, this phase could not be identified in thin films by Wolloch and co-workers 17. Using nuclear resonant inelastic X-ray scattering (NRIXS), these latter authors analysed the lattice dynamical contribution to the phase stability in FeRh and demonstrated that the AFM ground state was stabilized by phonon softening. With increasing temperature, the nearly equiatomic FeRh alloy of B2 structure undergoes a metamagnetic transition from the low-temperature AFM α'' to the high-temperature ferromagnetic (FM) α' phase close to room temperature (RT) 18-27. In the AFM phase, Fe atoms carry a magnetic moment of 3.3 μ B of alternating direction while the Rh atoms possess inconsiderable magnetic moment 22. Conversely, in the FM phase, Fe and Rh atoms carry parallel moments of 3.2 μ B and 0.9 μ B , respectively 28-30. This magnetic transition is accompanied with a reduction of the resistivity and an ~ 0.6% isotropic strain of the crystal lattice 21,31. By introducing strain in the FeRh crystal lattice, this phenomenon can be reversed and the mag...

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confidence: 91%

Reversible control of magnetism in FeRh thin films

Merkel

Lengyel

Nagy

et al. 2020

Sci Rep

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“…Memristive devices based on various mechanisms, such as ECM, [9,[17][18][19] VCM, [20][21][22][23] PCM, [24][25][26][27] TCM, [28,29] Mott transition, [30,31] photonic-induced switching, [32,33] ferroelectric transition, [34,35] and magnetic transition, [36,37] are investigated as emerging devices to mimic synaptic functions. Among them, ECM-based, VCM-based, and phase change-based memristors are most prevalent to fabricate large-scale CBAs for hardware implementation of ANNs.…”

Section: Artificial Memristive Synapsementioning

confidence: 99%

“…These desirable properties make memristors particularly attractive as neuromorphic devices (i.e., synapses and neurons). Nowadays, a large variety of memristive devices with different mechanisms, such as electrochemical metallization mechanism (ECM), [9,[17][18][19] valence change mechanism (VCM), [20][21][22][23] phase change mechanism, [24][25][26][27] thermochemical mechanism (TCM), [28,29] Mott transition, [30,31] photonic-induced switching, [32,33] ferroelectric transition, [34,35] and magnetic transition, [36,37] are investigated to mimic synaptic and neuronal functions. The variety of memristive devices facilitate the development of high-performance, large-scale, and energy-efficient NCSs.…”

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confidence: 99%

Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays

Ang

2020

Advanced Intelligent Systems

124

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Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector‐matrix multiplications and flexible tunable conductance, the memristor crossbar array (CBA) structure is one of the most promising candidates to realize neural cognitive systems. The boom in the development of memristive synapses and neurons has propelled the developments of artificial neural networks (ANNs) to emulate the highly hierarchically organized network of human brain in the past decade. To achieve this, realizing large scale, high‐density memristive CBAs is a prerequisite to constructing complex ANNs. Herein, the stringent requirements in device performance and array parameters for hardware ANNs are analyzed, and the efforts in addressing the associated challenges are discussed. Recent progress on the experimental demonstration of neuromorphic computing systems (NCSs) is presented. Recommendations for further performance optimization at the device, circuit, and algorithm levels are proposed. This Report serves as a guide for the hardware implementation of NCS based on large‐scale CBAs.

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“…for magnetic and spintronic applications, such as heat-assisted recording, 19-23 tunneling 24 and AFM-based memory devices, [25][26][27] magnetocalorics and barocalorics. 28,29 This AFM-FM transition has been widely studied as well from a fundamental point of view.…”

Section: New Conceptsmentioning

confidence: 99%