United States

The hysteresis modeling of ferromagnetic materials in electrical equipment is one of the basic theoretical studies in the field of electrical engineering. In this paper, a three-dimensional vector hysteresis model is proposed based on Stacked Auto-Encoder (SAE) model and hysteresis operator space theory. Multiple 3D vector Play hysteresis operators are used to construct an hysteresis operator space to create the high-dimensional operator data. By the operator space, the nonlinear hysteresis relationship in the hysteresis data is transformed into a nonlinear mapping between the operator data and the output of the model. And the SAE model is used to characterize this nonlinear mapping. In the training of the SAE model, training set is composed of operator data and magnetic field strength data. The structure of the vector hysteresis model is determined by taking the output of the operator space as the input of the SAE model. The simulation results show that the model can effectively describe the nonlinear characteristics and anisotropic of the Soft Magnetic Composite under 3D vector excitation. In addition, this model has certain generalization ability.&lt;br&gt;

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MMM 2022

The Study of the Three dimensional Vector Hysteresis Model based on the SAE model and Play operator

The hysteresis modeling of ferromagnetic materials in electrical equipment is one of the basic theoretical studies in the field of electrical engineering. In this paper, a three-dimensional vector hysteresis model is proposed based on Stacked Auto-Encoder (SAE) model and hysteresis operator space theory. Multiple 3D vector Play hysteresis operators are used to construct an hysteresis operator space to create the high-dimensional operator data. By the operator space, the nonlinear hysteresis relationship in the hysteresis data is transformed into a nonlinear mapping between the operator data and the output of the model. And the SAE model is used to characterize this nonlinear mapping. In the training of the SAE model, training set is composed of operator data and magnetic field strength data. The structure of the vector hysteresis model is determined by taking the output of the operator space as the input of the SAE model. The simulation results show that the model can effectively describe the nonlinear characteristics and anisotropic of the Soft Magnetic Composite under 3D vector excitation. In addition, this model has certain generalization ability. 

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poster

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We using Mumax3 to simulate a 5×5 asymmetric disk array. By changing the temperature, we explored the variety of energy during the evolution of the vortex and the probability of the vortex chirality. We also controled disc spacing to adjustment the magnitude of stray field . The material we used amorphous permalloy, simulation parameters were saturation magnetization 8.6x105 A/M, exchange length 13x10-12 J/m, damping constant 0.01, cell size 5x5x5 nm3, and temperatures changed from 0K to 300K. Geometric parameters were disk diameter 1000 nm, thickness 40 nm, asymmetric ratio 0.3, and disc spacing 200, 500 and 800 nm. In the previous research, vortex chirality seems to be related to a rise in total energy. When increase temperature will also increase the total energy in our simulation. We analyze the total energy, demagnetization energy and exchange energy separately then we finded the exchange energy increases significantly with temperature. It can be speculated that changes in temperature make the magnetic moments less parallel to each other, resulting in a significant increase in exchange energy and increase in the probability of CW vortices. 
**References** 
Arne Vansteenkiste, Jonathan Leliaert, Mykola Dvornik, et al. AIP Advances, 4, 107133(2014) 
Mi-Young Im, Ki-Suk Lee, Andreas Vogel, et al. NATURE COMMUNICATIONS, 5:5620(2014) 
Dustin A. Gilbert, Brian B. Maranville, Andrew L. Balk, et al. NATURE COMMUNICATIONS 6 8462 (2015) 
Mi-Young Im, Peter Fischer, Hee-Sung Han, et al. NPG Asia Materials 9 (2017) 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/23ee7e6067cfba89d83c2c80460bfab0.png) 

Fig1. Clock-wise vortex probability. 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/85be8c41c545d2fbcf95d1d138aac056.png) 
Fig2. MOKE image of disk array.

Study of temperature influences on the vortex chirality probability in asymmetric disk array

Magnetic vortex have attracted extensive attention in the field of information storage because of their topological spin structures with chiral bistable states[1,2]. If the vortex core polarity and vortex circulation sense can be precisely controlled simultaneously in a nanodisk, which will be more beneficial to realize the multi-bit ultrahigh density storage[3,4]. In this paper, a reliable control scheme for magnetic vortex chirality is proposed by optimizing the structure of Pac-Man-like nanodisk, as shown in Fig1(a)-(b). The results show that the polarity and circulation of the vortex can be precisely controlled simultaneously by simply changing the direction of the global magnetic field, and even the chiral states of the vortex can be determined by simply detecting the stray field distribution on the surface of the nanodisk, as shown in Fig1(c). The optimized Pac-Man-like nanodisk can provide a simplified experimental method for the control and detection of magnetic vortex chirality, which will be beneficial to the realization of multi-bit magnetic storage or magnetic logic technology in the future. 
**References** 
[1]H.-S. Han, S. Lee, D.-H. Jung, et al. Appl. Phys. Lett. 117(4), 042401(2020). 
[2]K. A. Zvezdin, E. G. Ekomasov. Phys. Met. Metallogr. 123(3), 201(2022). 
[3]J. A. Fernandez-Roldan, R. P. del Real, C. Bran, et al. Phys. Rev. B. 102(2), 024421 (2020). 
[4]D. Yu, J. Kang, J. Berakdar, C. Jia, NPG Asia Mater. 12(1), 1(2020). 
![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/40b03adf7fd13fe6cef5e9a8eb1d7001.png) 
Fig. 1. (a) and (b) show that the four chiral states of vortex can be precisely controlled. (c) It is shown that the vortex chirality can be determined by the distribution of the stray field on the
surface of the nanodisk.

Reliable control of magnetic vortex chirality in asymmetrically optimized magnetic nanodisk

Permanent magnet machines have many advantages such as high efficiency,high power density and so on,it have been widely used in the fields of aerospace,wind power generation and electric vehicles. However,permanent magnet machines have the disadvantage that the air-gap magnetic field is difficult to be adjusted.To effectively solve this problem,many scholars have proposed a hybrid excitation machine ,in which the electrically excited magnetic potential plays an auxiliary role in adjusting the air-gap magnetic field [1].The hybrid pole permanent magnet machine was proposed[2] ,which has a simple structure and high electric excitation efficiency.It has a permanent magnet and ferromagnetic pole structure on the rotor,but it has brush structure.A new hybrid excited consequent pole brushless Doubly Fed Machine (HE-CPBDFM) is proposed in this paper,and the performance of the machine is studied. Fig.1 gives the structure of the HE-CPBDFM.There are two sets of windings on the stator of the machine,a power winding with 3 pole pairs and a control winding with 1 pole pair;the number of permanent magnets on the rotor is the same as the number of poles of the control winding,and the polarity is the same,and the mechanical angle of the ferromagnetic pole with a smaller circumference between the two permanent magnets is 60°.The direction of the magnetic field synthesized by the control winding is facing the axis of the ferromagnetic pole,and the air gap magnetic field is adjusted by changing the magnitude of the excitation of the control winding. Keeping the external 220Ω load on the power winding side unchanged,the relationship curves between the power winding output voltage and the control winding excitation current under different excitation are shown in Fig.2. From Fig.2,it can be seen that the power winding output voltage increases with the increase of excitation current,and when the excitation current increases to 4A,the voltage growth rate slows down with the increase of machine saturation. 
**References** 
[1] Ullah S, McDonald S P, Martin R, et al. “A permanent magnet assist, segmented rotor, switched reluctance drive for fault tolerant aerospace applications,”. IEEE Trans. on
Ind. Appl., vol. 55, no. 1, pp. 298-305,Jan./Feb.2018. 
[2] Q. Zhang, S. R.Huang, G. D. Xie. “Design and experimental verification of hybrid excitation machine with isolated magnetic paths,”. IEEE Trans. Energy Conversion, vol. 25, no. 4,
pp. 993-1000, Dec.2010. 
[3]Ayub M, Jawad G, Kwon B. “Consequent-pole hybrid excitation brushless wound field synchronous machine with fractional slot concentrated winding,”. IEEE Trans. Magn., DOI:
10.1109/TMAG.2018.2890509,2019. 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/49f53eebc5552b4be295d867b745c562.png) 
Fig.1.Machine structure diagram 
![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/575bb0a645f2838d41805acb58bb6cbc.png) 
Fig.2.The relationship between the output voltage of the power winding and the excitation current of the control winding under different excitations

Design and research of the New Hybrid excited consequent pole Brushless Doubly Fed Machine

Fractals are composed of self-similar structures across different length scales, which look similar under different magnifications. They possess a non-integer dimension with a self-similarity and scale invariance, which is commonly referred to as the Hausdorff dimension or fractal dimension. Romanesque cauliflower, fern, frost, and snowflakes are popular examples of naturally observed fractals. The concept of fractals namely, Sierpinski square and triangles are also implemented in magnetic systems to study the modification of spinwave dynamics [1, 2]. It is also important to study their magnetization reversal process for their further use in spintronics. Here we have investigated the magnetization reversal process of a Sierpinski triangle which is a nonperiodic but ordered fractal structure. To understand the magnetization reversal process of Sierpinski triangles with the increase of the iteration level of the fractal structure are studied using a micromagnetic simulator namely object oriented micromagnetic framework (OOMMF) [3]. The magnetic parameters used for the simulation are taken from Ref. [1]. The hysteresis loop (M-H curve) is calculated for a structure going from simple geometric structures toward a Sierpinski triangle. TRI-0 is an equilateral triangle with the side length of 3 μm. To generate TRI-1, an equilateral triangle is removed from the center of TRI-0. To further generate, TRI-2 and TRI-3 an iteration process with a scaling factor of ½ is used. Due to the structurization, the coercivity and saturation magnetization are increased significantly with the increase of the iteration number as shown in Fig. 1(a). The simulated magnetization images during the reversal for Tri-0 and Tri-4 are shown in Fig. 1 (b) and (c) for three different magnetic fields. The interesting thing to note is that magnetization reversal occurs by the creation of a vortex. The observations have a great impact on their applications in spintronic and magnonic devices. 
**References** 
1. J. Zhou, M. Zelent, Z. Luo, V. Scagnoli, M. Krawczyk, L. J. Heyderman, S. Saha, Phys. Rev. B105, 174415 (2022) 
2. C. Swoboda, M. Martens, and G. Meier, Phys. Rev. B 91, 064416 (2015). 
3. M. Donahue and D. G. Porter, “OOMMF User’s guide, Version 1.0,” NIST Interagency Report No. 6376 _National Institute of Standard and Technology, Gaithersburg, MD, 1999_,
URL: http://math.nist.gov/oommf. 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/6a9590125a8d592aabcecf804a683feb.png) 

Fig. 1 (a) Simulated hysteresis loops for different samples. Simulated magnetization images at various applied magnetic field values marked by ‘1’, ‘2’ and ‘3’ for (b) Tri-0 (c) Tri-4 [a
single triangle is taken from the whole structure]

Magnetization reversal dynamics of a triangular shaped magnetic fractals

Analysis of magnetization reversal in ferromagnetic nanodots of different shapes with dimension in the range of 100 nm in the presence of Dzyaloshinskii-Moriya Interaction (DMI) is extremely important for technological application [1]. Though a few studies have explored the effect of DMI on the magnetization reversal mechanism in nanostructures, to disentangle the influence of shape and DMI requires further systematic studies [2-4]. Here, we investigate the effect of DMI (D) and in-plane bias field (H) on magnetization reversal in perpendicularly magnetized isolated square, circular, and triangular nanodot using the micromagnetic simulation Mumax3 [5]. The side length/diameter of the nanodot is chosen as 128 nm and the thickness as 1 nm and the material parameters of Co is selected. Our results indicate that the coercive field monotonically decreases as the strength of D increases and loop becomes asymmetric in the presence of both D and H in all three shapes. Interestingly, for D = 0 and H ≠  0, nucleation of the reverse domain occurs at the central region of the nanodot (Figs. 1(a-c)), and in case of D ≠  0 and H ≠  0 chiral nucleation occurs at the far edge (Figs. 1(d-f)). Due to lateral asymmetry of the triangular nanodot, the total energy value is smaller for the triangular nanodots (for -100 mT < H < +100 mT, and -1 mJ/m2 < D < + 1 mJ/m2) thereby making it preferred for energy efficient magnetization reversal as compared to other two shapes. However, at sufficiently higher value of the magnitude of H (> 200 mT) and D (> 1 mJ/m2), due to the laterally symmetric sides of the square the energy barrier for the magnetization reversal reduces drastically (cf. Fig.1(f)). These findings indicate that the effect of D and H in lowering the energy barrier for the magnetization reversal is intricately related to the symmetry associated with the shape of the nanodots. 

**References** [1] F. Hellman, A. Hoffmann, Y. Tserkovnyak et al., Rev. Mod. Phys., Vol.89, p.025006 (2017) 
[2] S. Pizzini, J. Vogel, S. Rohart et al., Phys. Rev. Lett., Vol.113, p.047203 (2014) 
[3] D.-S. Han, N.-H. Kim, J.-S. Kim et al., Nano Lett., Vol.16, p.4438 (2016) 
[4] Syamlal S K, Hari Prasanth Perumal, and Jaivardhan Sinha., Mater. Lett., Vol.303, p.130492 (2021) 
[5] A. Vansteenkiste, J. Leliaert, M. Dvornik et al., AIP Adv., Vol.4, p.107133 (2014) 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/55a7db279d3bf5b28b8f2b50d2d2ee8a.png) 
Figure 1: Snapshot of the spin configuration for square, circular and triangular nanodot during down to up (D-U) switching at H = -100 mT and D = 0 (a-c), H = -100 mT and D = 3 mJ/m2
(d-f). (g) Variation of total energy as a function of in-plane bias field for square, circular, and triangular nanodots at different value of D is plotted during D-U switching.

Effect of Dzyaloshinskii Moriya Interactions on Magnetization Reversal in Perpendicularly Magnetized Nanodots of Different Shape

The availability of large databases like materials project [1], Aflow consortium [2], NOMAD [3], OQMD [4] etc., has made it possible to apply machine learning (ML) in the materials science domain to get good predictions for various materials properties like Curie temperature [5], formation energy [6], etc. Here, we have used Materials project database to gather a data of 11545 iron-based compounds using the MAPI [7] and the pymatgen package [8]. This data consists of total magnetization, crystal structure and formation energy etc for all the compounds. As a first step, we transformed the data for ML modeling by using the orbital-field matrix (OFM) representation by Pham et al. [6]. In OFM, the structure of the compounds is turned into a 32*32 matrix corresponding to 1024 features, known as descriptor. With this data, we used various ML algorithms and chose to work with random forest as it provides the best results for our dataset with a 5-fold cross-validation. In order to understand the model, we used SHAP (SHapley Additive exPlanations) analysis, which is a game-theoretic approach to explain the output of any machine learning model. It gives us the most dominating features in the model. Based on the SHAP values for various datasets, the features which are dominating the overall magnetization results along with the corresponding description of the features as per the OFM descriptor are presented in Fig. 1. For example, in some oxides listed in Fig. 2, one can see that only features 173, 421, 429 are dominant as per the SHAP values. However, this is not a universally true picture for the whole dataset. Therefore, we further need to process the dataset based on the fact that not all features contribute equally in all types of materials. It is concluded that the simple ML techniques used on large dataset may give misleading results if the ML model is not improved after understanding the data we are dealing in the material science domain. 

**References** 
[1] A. Jain, P. Ong, G. Hautier, S.P. Ong, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson, Cite as APL Mater 1 (2013) 11002. 
[2] S. Curtarolo, W. Setyawan, S. Wang, J. Xue, K. Yang, R.H. Taylor, L.J. Nelson, G.L.W. Hart, S. Sanvito, M. Buongiorno-Nardelli, N. Mingo, O. Levy, Comput. Mater. Sci. 58 (2012)
227–235. 
[3] C. Draxl, M. Scheffler, J. Phys. Mater 2 (2019) 36001. 
[4] J.E. Saal, S. Kirklin, M. Aykol, B. Meredig, C. Wolverton, JOM 65 (2013) 1501. 
[5] T. Long, N.M. Fortunato, Y. Zhang, O. Gutfleisch, H. Zhang, Mater. Res. Lett. 9:4 (2021) 169–174. 
[6] T. Lam Pham et.al. Sci. Technol. Adv. Mater. 18 (2017) 756–765. 
[7] S.P. Ong, S. Cholia, A. Jain, M. Brafman, D. Gunter, G. Ceder, K.A. Persson, Comput. Mater. Sci. 97 (2015) 209–215. 
[8] S.P. Ong, W.D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V.L. Chevrier, K.A. Persson, G. Ceder, Comput. Mater. Sci. 68 (2013) 314–319. 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/3a8c31984a1254a4bb56ce58804ffe98.png) 
Fig. 1- Dominating features and corresponding orbital interaction. 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/51f54ea999db9bcb6857b01d22cee9f9.png) 
Fig. 2- Few compounds with magnetization values and valence band configuration.

Magnetization in Iron based compounds: A Machine Learning Model Analysis

Recently, artificial intelligence (AI) has received widespread attention, and artificial neural networks (ANN) are the foundation of AI, while artificial neurons are the basic information processing units of ANN. Spintronic devices mimicking the function of biological neurons are considered as one of the very promising candidates for the implementation of ANN in the future. In this work, we propose to realize the leaky integrate-and-fire (LIF) properties of artificial neurons through the voltage-controlled strain gradients and current induced spin-transfer torque co-driven the motion of domain wall (DW). In addition, the proposed device can be threshold-tuned in various ways, such as strain gradient, current density, and pulse width, etc. In particular, this structure can also break the limitation of ANN learning and recognition ability by the inherent activation function. By modulating the strain gradient applied on the nanowire, the relevant nonlinear activation function can be configured, thereby improving the learning performance of ANN. As shown in Fig. 1(a), the proposed device model consists of ferromagnetic/heavy metal (FM/HM) nanowire deposited on a piezoelectric substrate. By modulating the magnitude of the electrode voltage below the piezoelectric substrate, the voltage-controlled strain gradient applied to the nanowire will be changed, and finally affects the motion of DW. The corresponding magnetoconductance (TMG) can be read by the magnetic tunnel junction (MTJ). Fig. 1(b) shows the artificial neuron with the LIF function. The DW motion dynamics depend on the competition between the gradient magnetoelastic energy of the nanowire and the current driving force. The DW undergoes a total of five cycle current pulses, which finally reaches the threshold TMG of 0.63 and is detected by the MTJ. Next, the DW "fires" a signal output, and resets to the initial state. The above simulation results reveal the great potential of magnetic DW-based electric field modulation of artificial neurons for applications of next-generation artificial intelligence devices. 
![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/233bf41b35e517ba5f8c4e862fd62f13.png) 
Fig. 1 (a) Schematic diagram of voltage-controlled DW-based nanodevices. (b) LIF behaviors.

Voltage controlled domain wall

Skyrmions (Sks) are topologically protected chiral spin structures showing great potential for the next generation of data storing and processing devices.[1] However, controlling such chiral spin textures efficiently and precisely remains unsolved. Here, we demonstrate precise trajectory control of Sk motion by surface acoustic waves using micromagnetic simulations (Mumax3).[2] The computational region dimension was 400×100×1 nm3. The velocity, wavelength and amplitude of the SAW were 4000 m/s, 200 nm and 0.03, respectively. The Sk was initialised at different positions along the track. Results showed that with the application of the standing SAW, the Sk did not move if its initial position was at nodes of the standing SAW. Whereas starting from an anti-node, the Sk did move towards the closest node of the standing SAW (Fig.1a). The highest velocity occurs between the nodes and anti-nodes of the standing SAW, where the strain gradient is the highest, indicating that the strain gradient is the origin of the SAW-driven Sk motion.[3] The Sk moves continuously in the presence of the travelling SAW regardless of its initial position with a higher velocity (2.3 m/s) than that of the standing SAW-induced (average of 0.3 m/s) Sk motion (Fig.1b). This is due to the travelling SAWs providing a constant driving force to the skyrmion. The skyrmion also moves in the vertical direction owing to its chirality (Fig.1c). Horizontal travelling SAWs together with vertical standing SAWs were then applied to the Sk (Fig.1d), which can drive Sk motion and pin the Sk in the desired trajectory, respectively. Results showed that in the presence of both SAWs, the Sk moved along the centre of the nano-track with very limited vertical motion (Fig.1d to f). This study indicates the possibility of energy-efficient and precise control of skyrmion motion. 
**References** 
[1] Parkin et al. Science, 320(5873), 190-194 (2008) 
[2] Vansteenkiste et al. AIP Advances 4, 107133 (2014) 
[3] Nepal et al. Appl. Phys. Lett. 112, 112404 (2018) 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/d7b250c24cca457cb4f00186fd107fc4.png) 
Fig. 1 Skyrmion motion in the horizontal direction with standing (a) and travelling (b) SAW. (c) Spatial strain profile at 0 ns, skyrmion position at 0 to 16 ns, respectively. Skyrmion
motion in horizontal (d) and vertical (e) direction with horizontal travelling SAW and vertical standing SAW. (f) Spatial strain profile at 0 ns, skyrmion position at 0 to 6 ns, respectively.

Precise Trajectory Control of Skyrmion Motion by Surface Acoustic Waves

Magnetic skyrmion, with its nanoscale and topological features, can be employed as an information storage medium and logic function device in next-generation spintronic systems, attracting a lot of interest. Among all skyrmion logic devices, reconfigurable skyrmion logic devices have attracted much attention because they can efficiently perform different operations with the least number of devices to create different logical functions or operations. In 2018, Luo et al.1 proposed reconfigurable skyrmion logic gates by adding voltage control terminals to H-type nanotracks. This paper proposes a voltage-controlled reconfigurable skyrmion circular orbit logic gate in Fig1(f). The complete logic family is implemented based on our skyrmion logic gates, in which the trajectories of skyrmion are manipulated by various effects including skyrmion-edge repulsions and the voltage control of magnetic anisotropy (VCMA) effect. Fig.1(a) represents the unique orbital model, Fig.1(b)-(d) show the anisotropic energy regulated by the VCMA effect, and Fig. 1(e) shows the logic function realized by the potential energy well distribution that we studied before2. The above logic functions can be integrated into a single skyrmion device, reconfigured by controlling the terminal voltage, and reused by the circular track structure. The corresponding skyrmion state and motion trajectories are shown in Fig.2. Because of the scheme, every logic judgment necessitates the generation and destruction of the skyrmion, increasing the logic device's energy consumption. Furthermore, the current drive in this system will create Joule heat, which will have an impact on skyrmion stability. 
**References** 
[1] S. Luo, M. Song and X. Li, Nano Letters., vol. 18, pp. 1180-1184 (2018).
[2] L. Yang, F. Jin and W. Mo, IEEE Transactions on Magnetics., vol. 58, pp. 1-5 (2022). 
![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/f878e26ba3ade5d7664b09843a05c261.png) 
Fig.1 (a) Geometric dimensions of the circular track. (b)and(c) Voltage-controlled magnetic anisotropy gradient by using wedge structure. (d) Magnetic anisotropic energy distribution in
skyrmion multiplexing. (e) The arrangement of potential wells to realize logical functions. (f) Structure of a reconfigurable skyrmion logic gate with the circular track. 

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/62fabee36b4ea8e12b6cc70c156eebb0.png) 
Fig.2 (a) State and conversion process of skyrmion on the device. (b) The trajectories of skyrmion on the device.

Reconfigurable Skyrmion Logic Gates with Circular Track

In recent years, Racetrack memory[1] has been actively studied as a candidate of the future magnetic memory. Many papers on magnetic wire memories using ferromagnetic materials with large magnetization have been reported. Recently, it has been found that this current density can be reduced by using a ferrimagnetic material with small magnetization. However, the current density reduction of current-induced domain wall (DW) motion and improvement of the DW velocity remain as big issues for practical use. In this study, laser annealing (laser irradiation) effect on the ferrimagnetic wire is investigated to reduce further the current density. A magnetic wire of 1 μm width and 70 μm in length is prepared by using electron beam lithography and magnetron sputtering. The sample structure is Pt (5 nm) / Tb27Co73 (6 nm) / SiN (10 nm) on a Si substrate with a thermal oxide film (350 nm). It is placed in the field of view of polarizing microscope with an objective lens NA of 0.5, and a blue laser is irradiated to the TbCo wire through the objective lens. The spot size is approximately 1 μm in diameter which is almost same as the wire width. Laser annealing is scanned along the TbCo wire and the speed is 7 μm/s. Laser annealing can slightly change the DW energy of the TbCo wire. Fig. 1 shows DW velocity versus current density when the DW in the magnetic TbCo wire is driven by applying 3 ns pulse currents to the wire. Black squares and red circles are the symbols before and after laser annealing with the laser power of 3.1 mW. As shown in this figure, It was found that by laser annealing to the TbCo wire, the current density required for driving the DW was reduced by 20 %, and the DW velocity was doubled. Fig. 2 shows the mobility of the domain wall before and after laser annealing. Mobility is DW velocity divided by current density. After laser annealing, mobility is about 1.8 times higher than before. These may be due to the slight decrease in the DW energy caused by laser annealing. This work is partially supported by Grant-in-Aid for Scientific Research (20H02185 and 21K18735). 
**References** 
[1] S. S. P. Parkin, M. Hayashi and L. Thomas, Science, Vol. 320, p.190-194 (2008). 
[2] K. S. Ryu, L. Thomas and S.H. Yang, Nat. Nanotechnol., Vol. 8, p.527-533 (2013). 
[3] S. H. Yang, K. S. Ryu and S. Parkin, Nat. Nanotechnol., Vol. 10, p.221-226 (2015). 
![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/a4b943fbf63469c60f60d1c81a530fdb.png) 
DW velocity versus current density in TbCo wire. 
![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/ca2bf397c45d0328ca36ca935969a2d7.png) 
Comparison of the DW mobility before and after laser annealing with other reports[2][3].

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