United States

High-precision 3D reconstruction of multiple magnetic targets aims to reconstruct the three-dimensional shape, position and magnetic susceptibility of magnetic targets
based on discrete and noisy magnetic anomaly measurement data. It is widely used in anti-submarine, unexploded ordnance detection, subway magnetic pipeline detection and so on [1].
Currently, the research on three-dimensional reconstruction of multiple magnetic targets is still in the primary stage. Similar magnetic inversion technology is mainly studied in geological
exploration, including morphological inversion and physical inversion. For morphological inversion, it needs to know the magnetic susceptibility in advance, which leads to some
limitations in the reconstruction of unknown magnetic targets. For the physical inversion, because of the error of magnetic data and the lack of prior information, the effect of traditional
physical inversion method is not satisfactory [2]. In order to solve this problem, this paper proposes a center weighting 3D imaging method based on normalized cross-correlation
coefficient. The method selects magnetic gradient tensor data as observation data, calculates its cross-correlation coefficient with each cuboid cells. This can reduce the interference caused
by magnetic measurement noise and errors of magnetic measurement parameters [3]. And visualize the results to obtain the preliminary distribution of magnetic target (Figure 1). Then, the
spatial distribution and center position of the magnetic target are used as prior information to constrain the inversion equation. This can reduce the degree of freedom of the inversion
equation and get the optimal solution of the reconstruction result. Our simulation and experimental results show that the proposed method can realize 3D reconstruction of several simple
magnetic targets with a higher degree of fidelity (Figure 2).&lt;br&gt;

**References:**&lt;br&gt;

[1] Y. Wang, I. I. Kolotov, D. V. Lukyanenko et al, Computational Mathematics and Mathematical Physics, Vol. 60, p. 1000–1007 (2020)&lt;br&gt;
[2] G. Yin, L. Zhang, Journal of Magnetism and Magnetic Materials, Vol. 482, p. 229–238 (2019)&lt;br&gt;
[3] Y. Li, J. Song, H. Xia et al, Journal of Applied Physics, Vol. 127,104701 (2020)&lt;br&gt;

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MMM 2022

High precision 3D Reconstruction of Multiple Magnetic Targets Based on Center Weighting Method

High-precision 3D reconstruction of multiple magnetic targets aims to reconstruct the three-dimensional shape, position and magnetic susceptibility of magnetic targets
based on discrete and noisy magnetic anomaly measurement data. It is widely used in anti-submarine, unexploded ordnance detection, subway magnetic pipeline detection and so on [1].
Currently, the research on three-dimensional reconstruction of multiple magnetic targets is still in the primary stage. Similar magnetic inversion technology is mainly studied in geological
exploration, including morphological inversion and physical inversion. For morphological inversion, it needs to know the magnetic susceptibility in advance, which leads to some
limitations in the reconstruction of unknown magnetic targets. For the physical inversion, because of the error of magnetic data and the lack of prior information, the effect of traditional
physical inversion method is not satisfactory [2]. In order to solve this problem, this paper proposes a center weighting 3D imaging method based on normalized cross-correlation
coefficient. The method selects magnetic gradient tensor data as observation data, calculates its cross-correlation coefficient with each cuboid cells. This can reduce the interference caused
by magnetic measurement noise and errors of magnetic measurement parameters [3]. And visualize the results to obtain the preliminary distribution of magnetic target (Figure 1). Then, the
spatial distribution and center position of the magnetic target are used as prior information to constrain the inversion equation. This can reduce the degree of freedom of the inversion
equation and get the optimal solution of the reconstruction result. Our simulation and experimental results show that the proposed method can realize 3D reconstruction of several simple
magnetic targets with a higher degree of fidelity (Figure 2). 

**References:** 

[1] Y. Wang, I. I. Kolotov, D. V. Lukyanenko et al, Computational Mathematics and Mathematical Physics, Vol. 60, p. 1000–1007 (2020) 
[2] G. Yin, L. Zhang, Journal of Magnetism and Magnetic Materials, Vol. 482, p. 229–238 (2019) 
[3] Y. Li, J. Song, H. Xia et al, Journal of Applied Physics, Vol. 127,104701 (2020) 

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poster

#### Welcome to the 67th Annual Conference on Magnetism and Magnetic Materials, MMM 2022!

The Conference was held from October 31 to November 4 and offers both in-person and on-demand content.

MMM 2022 is sponsored jointly by AIP Publishing and the IEEE Magnetics Society. Members of the international scientific and engineering communities interested in recent developments in fundamental and applied magnetism are invited to attend and contribute to the technical sessions. The technical program will include invited and contributed papers in oral and poster sessions, invited symposia, a tutorial, and several special sessions. This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

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Please register for the MMM 2022 conference to join the event.

This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

Ultra-thin silicon steel with thickness less than 0.1 mm can be used to make the iron core of high-frequency transformers and high-speed motor. The design of these electrical equipment often relies on the data of alternating magnetic characteristics measured under standard excitation, ignoring the influence of working conditions such as stress and temperature, etc.. Under stress conditions, the magnetic properties of the magnetic material are quite different from those under standard sinusoidal excitation. Therefore, it is very important to accurately measure the magnetic properties of ultra-thin silicon steel under stress conditions.
Previously, many scholars have studied the influence of mechanical stress on the magnetic properties of conventional electrical steels [1-5]. However, there are few reports on the properties of high frequency materials under stress. When testing the properties of high frequency materials under stress, we can refer to the traditional testing methods of silicon steel. Compared with the other methods of stress application, such as motor or oil pressure, piezoelectric material has good flexibility and control accuracy, and it will not produce stray magnetic field to interference the measurement. The most important thing is that piezoelectric materials have good applicability to the magnetizer. In other words, it is not necessary to reconstruct the magnetic circuit structure and stress actuator, and the relevant experiments can be carried out directly by pasting piezoelectric materials in the sample.
This paper uses the magnetizer proposed in paper [6], which has good test accuracy. The sample is ultra-thin silicon steel with 0.1 mm thickness (GT100). The size of the sample is 50 mm×50 mm. Two Macro Fiber Composite (MFC) [7] are symmetrically pasted on the upper and lower surfaces of the sample center. A maximum tensile or compressive stress of about 150 N or 107 MPa can be obtained in the sample along one direction. The distribution of stress is calculated by the FEM and shown in Fig. 1. It can be seen that the stress is uniform in the measuring region. Fig. 2 gives the hysteresis loops under different stress along rolling direction. 

**References:** 

[1] Permiakov, V. , et al. "Loss separation and parameters for hysteresis modelling under compressive and tensile stresses." Journal of Magnetism & Magnetic Materials 272.supp-S(2004):E553-E554. 
[2] Singh, D. , et al. "Effect of stress on excess loss of electrical steel sheets." IEEE (2015):1-1. 
[3]Hameyer, et al. "Effect of mechanical stress on different iron loss components up to high frequencies and magnetic flux densities." COMPEL: The international journal for computation and mathematics in electrical and electronic engineering 36.3(2017):580-592. 
[4]Ali, K. , Atallah, K. and Howe, D. . "Prediction of mechanical stress effects on the iron loss in electrical machines." Journal of Applied Physics 81.8(1997). 
[5]Naumoski, H. , Maucher , A. and Herr, U. . "Investigation of the influence of global stresses and strains on the magnetic properties of electrical steels with varying alloying content and grain size." Electric Drives Production Conference IEEE, 2015. 
[6]Yue, S. , et al. "Comprehensive Investigation of Magnetic Properties for Fe–Si Steel Under Alternating and Rotational Magnetizations Up to Kilohertz Range." IEEE Transactions on Magnetics 55.7(2019):1-5. 
[7]Wilkie, W. K. , et al. "Low-cost piezocomposite actuator for structural control applications." Proceedings of Spie the International Society for Optical Engineering (2000). 

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Influence of stress on the magnetic properties of ultra thin silicon steel in high frequency range

Abstract—The Jiles-Atherton (J-A) model [1] predicts that an inductor has different inductances at the current ramp-up time (ton) and ramp-down time (toff). For the first time, this phenomenon is observed at the boost converter (Fig. 1) during the voltage converter (Fig. 2) and a simple equation to depict it is derived. Moreover, the methodology to calculate the inductor power loss from the voltage conversion data directly for a boost converter is presented.
I. Model and Experiment Result
From Fraday law, the inductance is
L=NdBA/(dt(dI/dt))=uNA d(H+M)/dI= Lo(1+dM/dH) (1)
From the J-A model, the differential of total magnetization M is
dM/dH=(Man-M)/((dk/m)-a(Man-M)) (2)
where d=1 if dH/dt>0 and d=-1 if dH/dt < 0.
From the experiment result in Figs. 3-5, Man-M can be approximated to a constant MD since the voltage almost keeps constant at each ton or toff based on L=V/(dI/dt). So, the inductance at ton is given by
L= Lo(1+mMD/(k-aMD)=L1 (3)
The inductance at toff is given by
L= Lo(1-mMD/(k+aMD)=L2 (4)
Figs. 3-5 show the voltage and current waveforms of the boost converter during the voltage conversion for the output currents (IOUT in Fig. 1) 0A, 0.2A and 0.4A. However, the three different IOUT waveforms have different ton and toff due to different slops by aligning their base current level with the current waveform for IOUT=0A as shown in Fig. 6. It proves that an inductor has the different inductances at ton and toff and L1 at ton is larger than Lo and L2 at toff is smaller than Lo. This is caused by the hysteresis effect as shown in Fig. 7 since it follows the initial hysteresis loop, minor hysteresis curves i and ii for Lo, L1 and L2. Integrating the powers at ton and toff and divided by the period T, the inductor power loss for boost converter during the voltage conversion is Ploss=(L1-L2)dIL(dIL+2IMIN)/2T (5)
where dIL is the peak-to-peak inductor current, IMIN is the minimum inductor current in Fig. 2.
Table-I shows the input power, measured inductor power loss based on Figs. 3-5 and calculated inductor power loss based on Eq. (5). The calculated power loss is very close to the measured power loss, verifying Eq. (5) valid to calculate the inductor power loss from the electrical voltage conversion data directly. 


**References:** 

[1] D. C. Jiles and D. L. Atheron, “Theory of Ferromagnetic Hysteresis,” J. of Magnetism and Magnetic Materials, pp. 48-60, 1986. 

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A New Inductor Power Loss Model for Boost Converter during the Voltage Conversion Based on the Jiles Atherton Model

Different contributions and mechanisms of the unidirectional magnetoresistance (UMR) have become a recent research hotspot in spintronics. Field sweeping DC measurements are used to measure the value of UMR instead of second harmonic measurements. [1,2] In this paper, potential measurement errors in conventional DC measurements are studied. Oersted field and field-like torque usually do not influence the measurement, but a large field-like torque can result in anisotropic magnetoresistance (AMR) difference when the sample is not perfectly aligned. The existence of ordinary magnetoresistance (OMR) can also contribute to a large background. An alternative measurement method is demonstrated to address this issue. This work broadens the understanding of the error sources and provides a standard measurement method for UMR DC measurements. 

**References:** 

[1] Duy Khang, Nguyen Huynh, and Pham Nam Hai. "Giant unidirectional spin Hall magnetoresistance in topological insulator–ferromagnetic semiconductor heterostructures." Journal of Applied Physics 126.23 (2019): 233903. 
[2] Chang, Ting-Yu, et al. "Large unidirectional magnetoresistance in metallic heterostructures in the spin transfer torque regime." Physical Review B 104.2 (2021): 024432.

Origins of potential observational errors in field sweeping DC measurements for unidirectional magnetoresistance

Amniotic fluid is a clear and pale yellowish liquid that surrounds and protects the fetus (unborn baby) throughout pregnancy [1]. The amniotic fluid analysis can diagnose certain health disorders in the unborn baby such as Down syndrome, Cystic fibrosis, Sickle cell disease, Tay-Sachs disease, and Neural tube defects [2]. Fig. 1 shows a 10-week-old human fetus surrounded by amniotic fluid within the amniotic sac [3]. 

This abstract presents a deep learning/machine learning pipeline that can be used for the prediction of health problems in an unborn baby by using deep learning or machine learning algorithm. The work is divided into two parts; (1) data collection, and (2) data processing and analysis. Fig. 2(a) shows data collection workflow. In the data collection, 20 samples of amniotic fluid are collected from a healthy and an unhealthy woman during 15~20 weeks of pregnancy [4]. After collecting the samples, the nuclear magnetic resonance (NMR) is performed for data acquisition [5]. Fig. 2(b) depicts workflow of data processing and analysis. In this part, metabolites data is normalized and a data reduction method like PCA is applied. After getting the preprocessed data, feature selection algorithms are performed to remove the irrelevant data. Three variable selection algorithms such as (i) Correlation-based feature selection (CFS), (ii) Partial least squares regression (PLS), and (iii) Learning Vector Quantization (LVQ) [4] are used. For each selected set of metabolites, support vector machine (SVM) models are generated using 10-fold cross-validation. To evaluate the performance and significance of the optimized model, we perform a permutation test. In the 10-fold cross-validation, the permutation test shuffles the class labels while using the same set of metabolites. For data analysis, we calculate true/false positives and true/false negatives for each round of our 10-fold cross-validation to report average sensitivity (TP/P) and specificity (TN/N) values along with the average of the area under the curve (AUC) [4]. 

**References:** 

[1]. C. M. Broek, J. Bots, I. V. Lasheras, M. Bugiani, F. Galis, S. V. Dongen, “Amniotic fluid deficiency and congenital abnormalities both influence fluctuating asymmetry in developing limbs of human deceased fetuses,” PLoS One, vol. 8, no. 11, 2013. 
[2]. MedlinePlus [Internet], National Library of Medicine (US), “Amniocentesis (amniotic fluid test),” Available from: https://medlineplus.gov/lab-tests/amniocentesis-amniotic-fluid-test/. 
[3]. https://en.wikipedia.org/wiki/Amniotic_fluid. 
[4]. R. O. B. Singh, A. Yilmaz, H. Bisgin, O. Turkoglu, P. Kumar, E. Sherman, A. Mrazik, A. Odibo, and S. F. Graham, “Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction,” PLoS One vol. 14, no. 4, 2019. 
[5]. https://www.jeol.co.jp/en/products/nmr/basics.html. 

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Analysis of Nuclear Magnetic Resonance Metabolomics data for the detection of unborn baby health problems using deep learning

Superparamagnetic nanoparticles (SNPs) are widely used in biomedical applications like imaging, magnetic hyperthermia, drug delivery, etc. [1-2]. We present a continuation of our study of the scattering of dilute aqueous suspensions of single-domain magnetite nanoparticles using an AC Faraday rotation setup [3-5]. The setup employs a stabilized He-Ne laser (633 nm) along with an AC magnetic field (130-1120 Hz) that enables lock-in detection. We have measured the scattering response of magnetite nanoparticles that vary in diameter from 15 to 25 nm. Our previous results analysed the scattering of light by SNPs subjected to an AC magnetic field, utilizing the even harmonics of the intensity signal. In this study, we continue to investigate the leading even harmonic (2f) signal to analyse scattering, but with added emphasis on the initial polarization angle of the incident light (linearly polarized) and its relationship to the orientation of the applied magnetic field. This approach allows us to investigate how the direction of the applied magnetic field results in a shape anisotropy for the SNP aggregates, and the effect that this anisotropy has on the scattering of light with varying angles of incident polarization. Differences in scattering are then related to the structural details of the aggregates as well as the time dynamics of the formation of these structures. The two orthogonal cases (transverse and longitudinal) that form the basis of this analysis are where incident polarization is perpendicular and parallel to the applied field (also the presumed long axis of aggregates), respectively. The scattering is also measured for various other angles between a parallel and perpendicular orientation. The model allows us to predict an angle for which the scattering is not affected by the application of the magnetic field. We show that the presence of this angle is confirmed by our data.
Our results provide important insight into the role of nanoparticle size (core and hydrodynamic), particle concentration, and magnetic field profile (intensity and frequency) in the formation dynamics of clusters. 

**References:** 

1. R. Hergt, S. Dutz, and M. Zeisberger, Nanotechnology, 21, (2010). 
2. Y. Xiao and J. Du, J. Mater. Chem. B, 8, 3, 354–367, (2020). 
3. S. Vandendriessche, W. Brullot, D. Slavov, V. Valev, and T. Verbiest, Appl. Phys. Lett., 102, (2013). 
4. C. Patterson, M. Syed, Y. Takemaura, Journal of Magnetism and Magnetic Materials, 451, 248-253 (2018).<br
5. M. Syed, W. Li, N. Fried, and C. Patterson, AIP Advances, 11, 015328 (2021).

Role of Aggregation Dynamics in Magneto Optical Scattering by Superparamagnetic Nanoparticles

In recent years there is a growing interest in synthesizes of novel iron-based nanoparticles and nanocomposites with high efficiency of thermal energy transfer suitable for use in magnetic fluid hyperthermia. The development of novel biocompatible magnetic nanoparticles (MNPs) for biomedical applications has been the subject of extensive exploration over the past two decades. Here we study the characteristics of carbon-coated ferromagnetic (Fe-Fe3O4)@C “core-shell” nanoparticles in samples with different concentration of iron synthesized by a solid-phase pyrolysis (SPP) of iron phthalocyanine (FeC32H16N8) molecules. The sample with higher iron concentration additionally annealed at 250°C under the oxygen media produces (Fe3O4) shell on Fe nanoparticles. The structural and magnetic properties of these nanomaterials were conducted using Scanning Electron Microscope (SEM), High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images with elemental mapping data, Raman spectroscopy, X-ray diffraction (XRD), magnetometry, electron paramagnetic and ferromagnetic resonances (EPR, FMR). The STEM image in Fig. 1 demonstrates a presence of Fe-Fe3O4 “core-shell” nanoparticles of order ~ 50 nm with oxide shells in carbon matrix. The ferromagnetic hysteresis loops with residual magnetizations and coercive forces is shown in Fig. 2 at two different temperatures. The characteristics of magnetic heating of water-based solution with different concentrations of synthesized nanocomposites under the influence of an external magnetic field have been studied. The magnetic characteristics such as saturation magnetization and coercivity as well as the specific absorption rate (SAR) make these materials attractive for magnetic hyperthermia applications.This work was supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 857502 (MaNaCa). The work at CSULA supported by the NSF CREST Grant #HRD-1547723. 

**References**

Fig. 1 High-resolution transmission electron microscopy images of iron/iron-oxide nanoparticles with core-shell architecture embedded in carbon matrix 


Fig. 2 Magnetization versus magnetic field shown for Fe-Fe3O4 nanoparticles at 3K and 300K

Syntheses and Characterization of Core Shell Iron

Magnetic force microscope (MFM) is a type of scanning probe microscope (SPM) that detects and images the gradient of the stray magnetic field of the observed sample. Therefore, the magnetic domain image is greatly affected by the surface morphology of the probe and the magnetic properties of the coated magnetic thin film. If the strong stray magnetic field for the permanent magnet material disturbs the magnetization of the magnetic probe, accurate observation of the magnetic domain structure is difficult. L10 ordered FePt alloy with high uniaxial magnetocrystalline anisotropyis thought to be an candidate probe material because of its high corrosion resistance and high coercivity[1]. It has also reported that the orientation of the FePt layer can be controlled by introducing MgO layer[2]. However, there are only a few reports of magnetic domain images observed by MFM probes using FePt film. In this study, the orientation of the FePt layer was controlled by using a MgO under layer with varying film thickness and element additions in order to observe high-resolution magnetic domain images. The FePt coated probes were prepared using an ultra-high vacuum magnetron sputtering system. A MgO buffer layer was deposited on Si cantilever and Si substrate at room temperature (R.T.). Then, FePt was deposited at a substrate temperature Ts of 873 K and they were annealed at 973 K. The surface morphology of the probes was observed by scanning electron microscope (SEM). The crystal structure was evaluated by X-ray diffraction (XRD), and the magnetic properties weremeasured usinga superconducting quantum interference device (SQUID) magnetometer. From XRD patterns, the fundamental (002) and the superlattice (001) peaks from L10 ordered FePt phase have been clearly observed atthe FePt probe by the introduction of MgO buffer layer, and a clear magnetic domain image was observed. However, when a Cu-doped MgO layer was used, the peak intensity from the L10ordered FePt phase increased and a magnetic domain image with even higher resolution was obtained. High-resolution mechanism will be discussed at the conference.

Preparation of probes for high resolution magnetic force microscopy by orientation control of FePt layer

Transcranial magnetic stimulation (TMS) [1] is a non-invasive pain-free medical technology clinically approved for treatment of drug-resistant depression [2, 3]. TMS uses
pulsed magnetic fields to induce eddy currents [4] in the brain. The biological mechanism of rTMS effects is tought to be mostly relying on the long-lasting changes of the neuronal
excitability and the brain functional plasticity, respoectively [5]. However, the effects of TMS-like fields on non-neuronal cells and artificial systems are almost unknown. The aim of this
study was to explore the potential of repetitive magnetic stimulation (RMS) performed by TMS devices in oncology, controllable drug release and regenerative medicine applications.
Using a standard TMS device “Magstim Rapid2”, we examined effects of >30 experimental regimes of RMS on viability and phenotype of various tumour (glioblastoma, pancreatic, liver,
colorectal [6] and breast cancers) and immune cells (microglia and macrophages). We also tested the effect of RMS on drug release from polymer nanoparticles and reactive oxygen species
generation (ROSG) in cancer and immune cells. Finally, we examined the combined effects of RMS and drugs targeting tissue growth.
RMS selectively modulated viability and functional polarisation of microglia and macrophages in a frequency/intensity-dependent manner and affected the proliferation/viability of cancer
cells (cancer type, frequency- and pulse number-dependent up- and downregulation). RMS induced triggering of drug release from nanoparticles, enhancement of ROSG and additive drug
effects.
Our pioneering findings demonstrate the potential of TMS technology repurposing for immunomodulation, cancer treatment, and drugs and nanomedicines treatment.
We thank Sydney Vital (for seed grant) and Medilink Australia (for providing the “Magstim”). The authors gratefully acknowledge the support of the Macquarie University FMHHS
Laboratory Operations Team. A.G. is thankful for support of her work by the Macquarie University Research Fellowship. 

**References:** 

1. A. T. Barker, R. Jalinous, and I. L. Freeston, "Non-invasive magnetic stimulation of human motor cortex," Lancet, vol. 1, no. 8437, pp. 1106-7, May 11 1985, doi:
10.1016/s0140-6736(85)92413-4. 
2. A. T. Barker, I. L. Freeston, R. Jalinous, and J. A. Jarratt, "Magnetic stimulation of the human brain and peripheral nervous system: an introduction and the results of an initial clinical
evaluation," Neurosurgery, vol. 20, no. 1, pp. 100-9, Jan 1987, doi: 10.1097/00006123-198701000-00024. 
3. P. M. Rossini et al., "Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and
research application. An updated report from an I.F.C.N. Committee," Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, vol. 126, no.
6, pp. 1071-1107, Jun 2015, doi: 10.1016/j.clinph.2015.02.001. 
4. M. Hallett, "Transcranial magnetic stimulation and the human brain," Nature, vol. 406, no. 6792, pp. 147-50, Jul 13 2000, doi: 10.1038/35018000. 
5. J. P. Lefaucheur et al., "Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS)," (in English), Clinical neurophysiology : official journal
of the International Federation of Clinical Neurophysiology, vol. 125, no. 11, pp. 2150-2206, Nov 2014, doi: 10.1016/j.clinph.2014.05.021. 
6. B. Heng, S. B. Ahn and A. Guller, "TMS-like Magnetic Fields Modulate Metabolic Activity of Hepatic and Colorectal Cancer Cells." IEEE Transactions on Magnetics: 1-1, doi:
10.1109/tmag.2022.3147219.

Beyond conventional neurostimulation: effects of TMS like magnetic fields on cells and nanomaterials and their potential applications in oncology and regenerative medicine

The continuous growth in the demand to store, process, and access data is one of the main drivers of spintronic research. Emerging big data and AI computing technologies require writing data in less time and storing them at a smaller scale, and consuming less energy [1]. Magnetic memories (MRAM) using spin-transfer torques have proven their place among leading non-volatile memory technologies and are currently in production for eFlash replacement applications [2]. In parallel, spin-orbit torques (SOT) have emerged and proven to be an efficient way of writing reliably magnetization at nanosecond time scale, broadening the scope of MRAM to applications that run close to the clock speed of the central processing unit, leading to novel spintronic memory and computing approaches [3], [4].
I will review across this presentation the fundamental characteristics of SOT and their use to switch perpendicularly magnetized magnetic tunnel junction (MTJ) devices. I will cover different challenges to take up SOT-MRAM from material and stack optimization to technology large-scale integration and circuit design [5]. After describing typical full-scale integration process of SOT-MRAM devices on 300mm wafers using CMOS compatible processes, I will discuss manufacturable field free methods and figures of merit for SOT-based memories. Finally, I will introduce bit-cell design considerations on scaling and performance in the perspective of circuit macro-design architectures [6], and discuss the interest to combine SOT, STT and voltage-gate (VCMA) effects to improve density and leverage performances [7]. I will conclude with opportunities to diverse SOT-MRAM application spectra, notably in the field of in-memory computing [8]. 

**References:** 

[1] G. Molas and E. Nowak, “Advances in emerging memory technologies: From data storage to artificial intelligence,” Appl. Sci., vol. 11, no. 23, (2021), [2] B. Dieny et al., “Opportunities and challenges for spintronics in the microelectronics industry,” Nat. Electron., vol. 3, no. 8, pp. 446–459 (2020), [3] Q. Shao et al., “Roadmap of Spin-Orbit Torques,” IEEE Trans. Magn., vol. 57, no. 7 (2021), [4] A. Manchon et al., “Current-induced spin-orbit torques in ferromagnetic and antiferromagnetic systems,” Rev. Mod. Phys., vol. 91, no. 3 (2019), [5] K. Garello, F. Yasin, and G. S. Kar, “Spin-orbit torque MRAM for ultrafast embedded memories: From fundamentals to large scale technology integration,” IEEE IMW 2019, pp. 2019–2022 (2019), [6] M. Gupta et al., “High-density SOT-MRAM technology and design specifications for the embedded domain at 5nm node,” Tech. Dig. - IEDM, vol. 2020-Decem, pp. 24.5.1-24.5.4 (2020), [7] Y. C. Wu et al., “Voltage-Gate-Assisted Spin-Orbit-Torque Magnetic Random-Access Memory for High-Density and Low-Power Embedded Applications,” Phys. Rev. Appl., vol. 15, no. 6, pp. 1–10 (2021), [8] J. Doevenspeck et al., “SOT-MRAM based Analog in-Memory Computing for DNN inference,” Dig. Tech. Pap. - Symp. VLSI Technol., vol. 2020-June, no. 1, pp. 2020–2021 (2020) 

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