Elucidation of the mechanism for maintaining ultrafast domain wall mobility over a wide temperature range

Ranjbar, Sina; Kambe, Sota; Sumi, Satoshi; Thach, Pham Van; Nakatani, Yoshinobu; Tanabe, Kenji; Awano, Hiroyuki

doi:10.1039/d2ma00273f

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…Recently, compensated ferrimagnets, such as GdFeCo and GdCo, have garnered significant interest due to the emergence of ultrafast magnetic dynamics at a momentum compensation point. [27][28][29][30] While previous studies have successfully estimated parameters in ferromagnetic materials, [5][6][7] both ferrimagnetic materials and their compositions stand as vital research subjects, akin to the parameters of the previously studied ferromagnetic materials, in the realm of estimation from magnetic imagery. We have determined the composition of TbCo from the generated LME states and have effectively estimated it from all the LME images.…”

Section: Article Pubsaiporg/aip/amlmentioning

confidence: 99%

Estimation of TbCo composition from local-minimum-energy magnetic images taken by magneto-optical Kerr effect microscope by using machine learning

Kuno,

Deguchi,

Sumi

et al. 2023

APL Machine Learning

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Recently, the incorporation of machine learning (ML) has heralded significant advancements in materials science. For instance, in spintronics, it has been shown that magnetic parameters, such as the Dzyaloshinskii–Moriya interaction, can be estimated from magnetic domain images using ML. Magnetic materials exhibit hysteresis, leading to numerous magnetic states with locally minimized energy (LME) even within a single sample. However, it remains uncertain whether these parameters can be derived from LME states. In our research, we explored the estimation of material parameters from an LME magnetic state using a convolutional neural network. We introduced a technique to manipulate LME magnetic states, combining the ac demagnetizing method with the magneto-optical Kerr effect. By applying this method, we generated multiple LME magnetic states from a single sample and successfully estimated its material composition. Our findings suggest that ML emphasizes not the global domain structures that are readily perceived by humans but the more subtle local domain structures that are often overlooked. Adopting this approach could potentially facilitate the estimation of magnetic parameters from any state observed in experiments, streamlining experimental processes in spintronics.

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Section: Article Pubsaiporg/aip/amlmentioning

confidence: 99%

Estimation of TbCo composition from local-minimum-energy magnetic images taken by magneto-optical Kerr effect microscope by using machine learning

Kuno,

Deguchi,

Sumi

et al. 2023

APL Machine Learning

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“…In fact, the ferrimagnetic material has exhibited higher DW mobilities compared to other magnetic materials [20][21][22][23]. Although field assisted CIDWM driven by STT and CIDWM driven by SOT in ferrimagnetic GdFeCo wires have been well 2 of 8 studied in earlier works [23,25,26], there is still no report on a comparison of CIDWM driven by STT and driven by SOT in such ferrimagnetic GdFeCo wires.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Lately, CIDWM in rare-earth transition metal (RE-TM)-based ferrimagnetic wires with magnetic perpendicular anisotropy (PMA) has been extensively investigated because fast DW dynamic at the angular momentum compensation point has been observed in these ferrimagnetic wires [20][21][22][23][24]. In fact, the ferrimagnetic material has exhibited higher DW mobilities compared to other magnetic materials [20][21][22][23]. Although field assisted CIDWM driven by STT and CIDWM driven by SOT in ferrimagnetic GdFeCo wires have been well 2 of 8 studied in earlier works [23,25,26], there is still no report on a comparison of CIDWM driven by STT and driven by SOT in such ferrimagnetic GdFeCo wires.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Current Induced Domain Wall Motion Driven by Spin Transfer Torque and by Spin Orbit Torque in Ferrimagnetic GdFeCo Wires

Thach,

Sumi,

Tanabe

et al. 2024

Magnetochemistry

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Current-induced domain wall motion (CIDWM) in magnetic wires can be driven by spin transfer torque (STT) originating from transferring angular momentums of spin-polarized conducting electrons to the magnetic DW and can be driven by spin orbit torque (SOT) originating from the spin Hall effect (SHE) in a heavy metal layer and Dzyaloshinsky Moriya (DMI) generated at an interface between a heavy metal layer and a magnetic layer. In this work, we carried out a comparative study of CIDWM driven by STT and by SOT in ferrimagnetic GdFeCo wires with magnetic perpendicular anisotropy based on structures of SiN (10 nm)/GdFeCo (8 nm)/SiN (10 nm) and Pt (5 nm)/GdFeCo (8 nm)/SiN (10 nm). We found that CIDWM driven by SOT exhibited a much lower critical current density (JC), and much higher DW mobility (µDW). Our work might be useful for the realization and the development of low-power and high-speed memory devices.

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“…17 Second, the perpendicular anisotropy field H p of the FiM can be manipulated, thus enabling control over the critical current required for SOT driven magnetization switching. 18 Third, the opposite angular momenta of Gd and Fe make it possible to control the net angular momentum and, thereby, manipulate domain wall velocities in the FiM, allowing for the realization of faster domain wall motion, 19,20 leading to a more efficient SOT driven magnetization reversal. All the above advantages make HM/FiM bilayers a promising candidate for realizing ultrafast and efficient spin logic devices.…”

Section: Introductionmentioning

confidence: 99%

Realization of logic operations via spin–orbit torque driven perpendicular magnetization switching in a heavy metal/ferrimagnet bilayer

Jacob Mathew,

Gao,

Wang

et al. 2024

Journal of Applied Physics

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Programmable and non-volatile spin-based logic devices have attracted significant interest for use in logic circuits. Realization of logic operations via spin–orbit torque (SOT) driven magnetization switching could be a crucial step in the direction of building logic-in-memory architectures. In this work, we demonstrate experimentally, the realization of four logic operations in a heavy metal/ferrimagnet bilayer structure via SOT switching. We also propose a general scheme for choosing input parameters to achieve programmable logic operations. The bulk and tunable perpendicular magnetic anisotropy and relatively lower saturation magnetization in ferrimagnets are found to make them more energy efficient in performing logic operations, as compared to conventional ferromagnets. Thus, ferrimagnets are promising candidates for use in logic-in-memory architectures, leading to the realization of user-friendly spin logic devices in the future.

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Elucidation of the mechanism for maintaining ultrafast domain wall mobility over a wide temperature range

Abstract: In order to achieve a 20-Gbps data rate using the upcoming 5G communication standard, it is crucial to recognize a domain-wall (DW) velocity (vDW) of 1200 m/s. We demonstrate a...

Cited by 9 publications

References 31 publications

Estimation of TbCo composition from local-minimum-energy magnetic images taken by magneto-optical Kerr effect microscope by using machine learning

Estimation of TbCo composition from local-minimum-energy magnetic images taken by magneto-optical Kerr effect microscope by using machine learning

Comparison of Current Induced Domain Wall Motion Driven by Spin Transfer Torque and by Spin Orbit Torque in Ferrimagnetic GdFeCo Wires

Realization of logic operations via spin–orbit torque driven perpendicular magnetization switching in a heavy metal/ferrimagnet bilayer

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