Large spin-to-charge conversion in ultrathin gold-silicon multilayers

Hadri, Mohammed Salah El; Gibbons, Jonathan; Xiao, Yuxuan; Ren, Haowen; Arava, Hanu; Liu, Yuzi; Liu, Zhaowei; Petford‐Long, Amanda K.; Hoffmann, A.; Fullerton, Eric E.

doi:10.1103/physrevmaterials.5.064410

Cited by 3 publications

(2 citation statements)

References 69 publications

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“…98 However reducing the film thickness is limited by the requirement of having continuous films for transport. It was shown that this limitation can be circumvented by using Si/Au multilayers, 99 where the gold thickness can be reduced down to the nanometer-regime, which results in spin-charge conversion efficiencies or order unity, see Fig. 5(a).…”

Section: B Magnetization Dynamicsmentioning

confidence: 99%

Quantum materials for energy-efficient neuromorphic computing

Hoffmann,

Ramanathan,

Grollier

et al. 2022

Preprint

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Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

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Section: B Magnetization Dynamicsmentioning

confidence: 99%

Quantum materials for energy-efficient neuromorphic computing

Hoffmann,

Ramanathan,

Grollier

et al. 2022

Preprint

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“…By engineering the bulk spin Hall effect (SHE) in HMs [7,8] and interfacial Rashba-Edelstein effect (REE) [9][10][11], enhanced SOT values can be achieved that have the potential for developing novel energy-efficient magnetic memory [12], logic [13], and neuromorphic computing devices [14]. Conventional SOT studies mainly focus on textured HMs such as Pt [15], Au [16,17], β-W [18,19], and β-Ta [20], and transition metal alloys, for example, Cu-Ta [21] and Fe-Pt [22]. More recently, epitaxial materials with tunable crystalline anisotropy and welldefined orientations have been recognized as promising candidates for SOT studies [23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Crystalline Orientation–Dependent Spin Hall Effect in Epitaxial Platinum

2022

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We report on the spin Hall effect in epitaxial Pt films with well-defined crystalline (200), (220), and (111) orientations and smooth surfaces. The magnitude of the spin Hall effect has been determined by spin–torque ferromagnetic resonance measurements on epitaxial Pt/Py heterostructures. We observed a 54% enhancement of the charge-to-spin conversion efficiency of the epitaxial Pt when currents are applied along the in-plane <002> direction. Temperature-dependent harmonic measurements on epitaxial Pt/Co/Ni heterostructures compared to a polycrystalline Pt/Co/Ni suggest the extrinsic mechanism underlying spin Hall effect in epitaxial Pt. Our work contributes to the development of energy-efficient spintronic devices by engineering the crystalline anisotropy of non-magnetic metals.

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Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

et al. 2022

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Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

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Large spin-to-charge conversion in ultrathin gold-silicon multilayers

Cited by 3 publications

References 69 publications

Quantum materials for energy-efficient neuromorphic computing

Quantum materials for energy-efficient neuromorphic computing

Crystalline Orientation–Dependent Spin Hall Effect in Epitaxial Platinum

Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

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