Spintronics-based devices offer certain key advantages like ease of fabrication with Si-substrate, non-volatile memory, low operational voltage, and non-linear device characteristics. Hardware security is a research domain that heavily relies on CMOS-based ICs, and the defense and attack mechanism is developed accordingly.
In this work, we explore shape anisotropy-based PMA Double barrier MTJ based on Verilog-A behavioral model (1) to design a possible Logic locking (2) system for Hardware Security. Logic Locking is a widely used security mechanism to counter multiple threats (3). Due to larger free layer thickness, the s-PMA DMTJ has better thermal stability even in the sub-10nm dimension. The current work focuses on Logic locking applications using this device, analyze its behavior under process variation in certain key parameters using Monte-Carlo simulations, and finally performs Eye-diagram performance analyses to test the design for high-speed digital logic applications. The results indicate that the s-PMA DMTJ has good thermal stability, smaller size (10nm dimension), robustness to device imperfections, and ability to work in high-speed digital circuits compared to other emerging MTJ structures such as STT MTJ, SOT MTJ, VG-SOT MTJ, etc.
Fig. 1(a) represents the block diagram of the logic Locking block, and Fig. 1(b) represents the circuit for implementing the desired operation. We have performed electrical simulations in TSMC 40nm CMOS generic process design kit using the cadence specter simulator with W/L ratio = 3, the temperature at 300K. Fig. 2(a) and 2(c) represent some circuit performance comparisons like the Eye-diagram test for high-speed circuits, and Table 1 presents the simulation data for the same. Fig. 2(b) shows that VG-SOT MTJ (4) fails the eye-diagram test for similar applications for comparison with s-PMA DMTJ. 

**References:** 
(1) H. Wang et al., “Modeling and Evaluation of Sub-10-nm Shape Perpendicular Magnetic Anisotropy Magnetic Tunnel Junctions,” IEEE Transactions on Electron Devices, vol. 65, no. 12, pp. 5537-5544, Dec. 2018. 
(2) H. M. Kamali, K. Z. Azar, F. Farahmandi, and M. Tehranipoor, “Advances in Logic Locking: Past, Present, and Prospects,” Cryptology ePrint Archive, 2022. 
(3) S. Bhunia, and M. Tehranipoor, “Hardware security: a hands-on learning approach,” 1st Edition, 2018. 
(4) K. Zhang, D. Zhang, C. Wang, L. Zeng, Y. Wang and W. Zhao, “Compact Modeling and Analysis of Voltage-Gated Spin-Orbit Torque Magnetic Tunnel Junction,” IEEE Access, vol. 8, pp. 50792-50800, 2020. 
(5) K. Watanabe et al., “Shape anisotropy revisited in single-digit nanometer magnetic tunnel junctions,” Nature Communication, 2018. 

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Double Barrier s PMA MTJ

Magnetic multilayers are material systems that offer a high degree of tunability with respect to their magnetic and electronic properties. These are essential for today’s spintronics and magnonics-based magnetic applications [1], [2]. The Dzyaloshinskii-Moriya interaction (DMI) [3] is one of the interesting phenomena in such multilayers that give rise to the presence of magnetic skyrmions under certain conditions even at room temperature. Pt/Co is one of the most promising classes of the multilayers that contributes to a significant DMI due to the different characters of Pt/Co and Co/Pt interfaces [4]. The performance of the films may vary depending on the substrates, deposition temperature, layer thickness, and other factors in the Pt/Co multilayers. The thermal stability can be significantly increased and the magnetic anisotropy of Pt/Co multilayers can be controlled by adding spacer layers between two Co layers [5].
In our study, we investigate magnetic bubbles in Pt/Co-based multilayer thin films. The bubbles have been expanded using an external magnetic field both in-plane and out-of-plane. In presence of the in-plane field, bubbles expand asymmetrically as observed in Fig.1(a). The in-plane field-dependent velocities of two opposite chiral domain walls are measured as shown in Fig. 1(b). The asymmetry of bubble expansion is found to be influenced by the thickness of Co which gives us information about the intrinsic chiral properties of the system. Mumax simulations are performed to understand underlying behavior. The study is important for race track memory applications. 
**References** [1]. R. E. Camley, and R. L. Stamps, “Magnetic multilayers: spin configurations, excitations, and giant magnetoresistance,” Journal of Physics: Condensed Matter, vol. 5, no. 23, 1993.
[2]. E. Y. Vedmedenko, R. K. Kawakami, D. D. Sheka, P. Gambardella, A. Kirilyukand, A. Hirohata, C. Binek, O. Chubykalo-Fesenko, S. Sanvito, and B. J. Kirby, “The 2020 magnetism roadmap,” Journal of Physics D: Applied Physics, vol. 53, no. 45, 2020.
[3]. T. Moriya, “Anisotropic Superexchange Interaction and Weak Ferromagnetism,” Phys. Rev, 1960.
[4]. A. W. J. Wells, P. M. Shepley, C. H. Marrows, and T. A. Moore, “Effect of interfacial intermixing on the Dzyaloshinskii-Moriya interaction in Pt/Co/Pt,” Phys. Rev. B, vol. 95, 2017.
[5]. S. Eimer, H. Cheng, J. Li, and X. Zhang, “Perpendicular magnetic anisotropy based spintronics devices in Pt/Co stacks under different hard and flexible substrates,” Science China. Information Sciences, 2021. 
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Asymmetric Pt/Co magnetic multilayers bubble expansion under in plane and out

Spintronics is a prime candidate for information storage and logic devices due to its significant advantages over traditional CMOS technology, such as low-power consumption, non-volatility, and high endurance. Recently, spin-gapless semiconductors (SGSs) have been introduced for modern spintronics-based applications and are promising candidates for designing quantum computing, data storage, coding, and decoding. In SGSs, spin-polarized charge carriers or spin current are the primary functions of these magnetic materials [1]. SGS materials such as CoFeMnSi Heusler Alloy offer high spin polarization (64%), high Curie temperature (620 K), and low damping constant (0.0046) are all intriguing properties that can be used in room-temperature spintronic device applications [2]. SGS has unique features such as almost temperature-independent carrier concentration, and quantum linear magnetoresistance in a low-temperature range [3].
In this abstract, the effect of changing the sputtering power from 30W to 50W and 90W on an RF sputtered thin film of CoFeMnSi (CFMS) Heusler alloy with a nominal thickness of 30 nm with temperature-dependent electrical and magneto-transport properties has been studied. Fig. 1(a) shows Hall resistivity as a function of applied magnetic field for CFMS 90W (30nm) thin-film recorded at 10K, 50K, 100K, 200K, 250K, and 300K. Fig. 1(b) demonstrates the SGS behavior of the material in nearly temperature-independent carriers' concentrations with the same order up to 300K. Fig. 1(c) depicts magnetoresistance (MR) curves of 90W (30nm) CFMS thin film within the temperature range of 10K to 200K. The MR curves exhibit a linear field-dependent trend. In this temperature range, the observed MR is negative. The peak presence at zero magnetic fields signifies the localization and as we increase the field, the path contributing to localization is go out of the phase. 
**References** [1]. D. Rani, L, Bainsla, A. Alam, and K. G. Suresh, “Spin-gapless semiconductors: Fundamental and applied aspects”, Journal of Applied Physics, vol. 128, no. 22, 2020.
[2]. L. Bainsla, A. I. Mallick, M. Raja, A. K. Nigam, B. Varaprasad, Y. Takahashi, A. Alam, K. G. Suresh, and K. Hono “Spin gapless semiconducting behavior in equiatomic quaternary CoFeMnSi Heusler alloy”, Physical Review B, vol. 91, 2015.
[3]. X. L. Wang, “Proposal for a new class of materials: spin gapless semiconductors”, Physical review letters, vol. 100, 2008. 
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Study of Cobalt Based Spin Gapless Semiconducting Heusler Alloy Thin Film

Understanding the behavior of free-living marine organisms is pivotal for many conservation efforts (REF) and examining how to change, such as overfishing and climate variability, affect marine ecosystems [1]. For this, many researchers have turned to animal-attached tag technology [2] where study animals carry archival â€œsmartâ€ tags that record a suite of parameters to quantify particular aspects of their behavioral ecology or physiology. It was demonstrated that the Pelagic jellyfish blooms are increasing worldwide as a potential response to climate change. The results shown in [3] suggest an enhanced growth of the jellyfish in response to global warming, whereas low temperatures may set the limits for successful invasion of the jellyfish into colder water bodies. Therefore, we are developing an underwater reliable wireless marine monitoring system composed of a flexible magnet (NdFeB-PDMS) and an MTJ-based sensor as shown in Fig. 1 in order to count the number of the jellyfish contractions under different environmental conditions. The fabrication process of the NdFeB-PDMS composite magnet involves minimal complexity, has lower cost, and is highly versatile with respect to the shape of the magnets [4] which play the role of a monitor of any jellyfish movement that would be detected by our MTJ-based sensor over time as can be seen in Fig 2. The composite magnet is coated with Paralyne C to protect the composite from degradation, make the remanent magnetization constant, and provide resistance against corrosion and biofouling, flexibility, and biocompatibility. The results show the suitability of the composite magnets for marine applications. Our results provide a framework for understanding the physiological tolerance of Cassiopea under possible future climate changes, which can be better interpreted by statistical coming studies using a wireless marine monitoring system based on our MTJ sensors.

MTJ based Marine Sensor for Jellyfish Cassiopea Response to the Environmental Change

Airborne particulate matter (PM) have been significantly increasing over the past two decades carrying significant health risks such as premature death, damage to the lungs, heart tissue, and even cancer. PM2.5 is the main concern as it has the most harmful health effects while being more difficult to detect [1]. Moreover, these magnetic nanoparticles are readily absorbed into the bloodstream causing neurodegenerative diseases such as Alzheimerâ€™s and cancerous tumors [2]. Therefore, it is essential to monitor the magnetic portion of PM and its adverse effects on health [3], [4]. This work focuses on developing, modeling, and simulation of a new kind of magnetic sensor that can count and localize these magnetic nanoparticles. Additionally, we have fabricated a novel low-noise and high-precision readout circuit for tunneling magnetoresistive (TMR) array to evaluate the bio-magnetic measurement platformâ€™s suitability for detecting weak bio-magnetic fields. The proposed sensors could help to prevent these nanoparticles from the polluted environment and undoubtedly reduce their adverse risks to humans. The modeled magnetic system consists of a TMR sensor array, a conducting line, and the detected magnetite nanoparticles as shown in Fig. 1(a). The localization and quantization of these Fe3O4 nanoparticles (characterized in Fig. 1(b)) can be achieved by analyzing total output voltages from the TMR sensor array. The Fe3O4 magnetite is injected inside the small closed chamber and they are exerting an external magnetic field that is aligned or opposed to the pinning field, which results in shifting the MTJ curve as seen in Fig. 1(c). After applying a current in the conducting line and creating an external field, the magnetite gets polarized and approaches the MTJ sensors. This technique can detect 50-200 nm magnetic particles compared to existing commercial monitoring solutions that are limited to 1-3Âµm particles.

High performance MTJ

Early cancer detection greatly enhances the prognosis and allows for higher treatment success. Cancer metastasis, which occurs in later stages, is believed to cause more than 90% of cancer-related deaths [1]. In earlier cancer stages, circulating tumor cells (CTCs) may be detected in the blood of cancer patients, which can correlate with patient survival rate. Accurate, non-invasive separation of these cells allows for early detection of cancer and aids in increasing therapeutic efficacy [2]. This work provides an integrated on-chip quantitative cell sorting platform that separates and counts magnetically labeled cells through a microfluidic channel. First, an immunomagnetic assay is utilized to label CTCs with highly selective magnetic nanoparticles. Then, magnetically labeled cells are separated and guided through the microfluidic channel by electromagnetics and ferromagnetic strips [3]. After cell separation, magnetically labeled biomarkers are detected by the magnetic tunnel junction (MTJ) sensor that measures stray fields surrounding the labeled CTCs, generating an electrical signal for each passing particle. Additionally, biomarkers with different sizes, such as CTCs and protein-based cancer biomarkers, can be detected and quantified as they move through the microfluidic channel at different speeds, yielding distinct peaks at the MTJ output [3][4]. This work demonstrates a highly selective platform that actively sorts magnetically labeled biomarkers by combining magnetophoresis-based cell sorting and MTJ-based flow cytometry allowing for highly sensitive quantitative on-chip cell sorting.

On chip Quantitative Cell sorting based on High Performance Magnetic Tunnel Junction

Flow cytometry is a powerful technique that enables cell quantification and analysis. As a result, it has become an essential instrument in biomedical research and clinical testing for disease diagnosis or treatment monitoring. However, the current technique is large and expensive, driving research into smaller, cheaper, and more efficient flow cytometers [1]. One approach to tackle this problem can be with the application of spintronic sensors such as giant magnetoresistance sensors for cell quantification [1][2]. In this work, we report a highly sensitive flow cytometer based on magnetic tunnel junction (MTJ) technology. After external labeling of cells with magnetized beads placed above the MTJ sensor to measure stray magnetic field surrounding the beads. When labeled cells pass through the sensitive area, a signal peak will be generated at the output of the MTJ sensor [3]. Alternatively, magnetic flow cytometers can evaluate the efficacy of certain cancer immunotherapies where cancer cell membranes coat magnetic nanoparticles to activate a specific natural killer (KT) cell response [4]. One of the cytometer applications is Live/dead cancer cells discrimination. This cytometer can detect the presence of these magnetic nanoparticles to ensure proper activation of KT cells and thorough removal of the modified magnetic particles. This result demonstrates a novel MTJ-based, highly sensitive flow cytometer design for quantifying magnetically labeled cells and cancer therapy involving magnetic nanoparticles. Using our suggested device, we can distinguish and separate between cancer cells and prove Live/dead cancer cells discrimination.

An Integrated and Highly Sensitive Magnetic Tunnel Junction based Cytometer

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

Magnetic Skyrmions are topologically protected swirling structures induced by chiral interactions in non-centrosymmetric magnetic compounds or thin films with broken inversion symmetry [1]. The spintronic devices based on these textures promise increased density and energy efficient data storage due to small nanometric size and topological protection [2][3]. In this work we present the effect of DMI, surface anisotropy, magnetic field and spin orbit torque (SOT) on the creation and annihilation of the skyrmions. Fig.1 (a) shows that the skyrmion density is increased as we increase the magnitude of DMI coefficient from 1mJ/m2 to 3mJ/m2. But with increasing the surface anisotropy the density is reduced and skyrmions are annihilated at some threshold anisotropy Kth which is proportional to the magnitude of the DMI coefficient. Furthermore, the size of skyrmion decreases with increasing anisotropy as shown in Fig. 1(b). In Fig. 1 (c-d) we study the effect of external magnetic field on the skyrmions. Fig. 1 (c) shows the MOKE images of the fabricated sample depicting the transition of magnetic texture from Labriynth domains to skyrmions around 8 Oe. The skyrmion density increases proportionally with field till 20 Oe and begins to roll off after 20 Oe. These results are supported by MuMax simulations as shown in Fig. 1(d). These results suggest that by proper optimization of material parameters and stack geometry we can tweak the skyrmion density for its application in memory and logic. In Fig. 2(a) we show MTJ with skyrmions in the free layer, using SOT control we propose this device for its application in neuromorphic computing. The SOT manipulation of skyrmion size, density and motion is shown in Fig. 2 (b). Increasing current Ic via the heavy metal increases the skyrmion density from 21µm-2 (at 2×1011 A/m2) to 40 21µm-2 (at 8×1011 A/m2). Further increase in Ic results in decrease of the skyrmion density. Finally, we implement a simple neuromorphic circuit using the proposed device.

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Fig. 1: (a) Skyrmion density vs anisotropy and DMI coefficient. (b) Density and size of skyrmion vs anisotropy. (c) MOKE images (d) MuMax simulations.

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(a) Skyrmion-MTJ neuromorphic device. (b) Skyrmion density vs current density

Creation and Annihilation of Skyrmions for Neuromorphic Computing Applications

The spintronic devices such as the domain wall and skyrmion based devices are being explored for their applications in neuromorphic computing [1-3], due to their non-volatile behaviour, stochastic switching and energy efficiency. The electric field control of these devices has attracted extensive attention and has further gained popularity both in memory and logic applications as it provides an efficient way to improve the data storage density [4-8]. Neuromorphic computing has been shown to be a superior alternative in terms of speed and energy, when compared to training on a conventional computing unit. A magnetic tunnel junction (MTJ), which constitutes the building block of a magnetic random-access memory (MRAM), is a competitive candidate for the next generation memory devices because it is non-volatile and exhibits high endurance, low power consumption, high operation speed and integration capability [8-9]. The present work discusses the proposal of a spintronic neuromorphic system with spin orbit torque driven domain wall motion-based neuron and synapse. The domain wall motion in the neuron is controlled by the electric field at the gate generated by voltage signals from pre-neurons as shown in Fig. 1(a). The application of voltage as input and output variable helps in reducing power consumption, as pinning site can be turned ON/OFF only when required. Further it provides better fanout as post-neuron output from first stage can act as infinite number of pre-neurons to next stage, which is going to play an important role in realization of system level neuromorphic architectures. For the implementation of the synaptic weight, we propose 3- terminal MTJ with stochastic domain wall motion in the free layer. The edge roughness results in intrinsic pinning of domain wall and this adds to the stochastic behaviour of domain wall motion in presence of spin transfer torque and/or spin orbit torque [10]. We have incorporated these intrinsic pinning effects by creating triangular notches on the sides of free layer as shown in Fig. 1(b). The complete neuromorphic circuit containing neurons, synapses and control circuitry implementation of spike time dependent plasticity is shown in Fig. 1(a). The sensing unit consists of a voltage comparator which provides high positive or zero voltage feedback pulse to the weight-updating unit. The weight updating unit takes this signal and the pre-neuron signal to decide the sign of the synaptic weight update pulse based upon spike timing correlation between the pre- and post-neuron. For modelling the micromagnetics and spin transport in both synapse and neuron, we developed a micromagnetic Non-Equilibrium Green’s Function (MuMag-NEGF) coupled model. In Fig. 2(a), we show that in the presence of the gate voltage the uniaxial anisotropy for a 20 nm-wide region is varied slightly which results in the pinning of domain wall at 40 nm from the centre. In the absence of gate voltage uniaxial anisotropy is uniform and the domain wall moves more than 100 nm right in 10 ns, where it is sensed by the MTJ as post neuron spike. Also, the reduction of domain wall velocity is shown in Fig. 2(b). The thermal effects result in the stochastic domain wall motion but stochasticity is tuned by the external current in the form of spin orbit torque. Fig. 2 (c) shows the stochastic magnetization switching in MTJ synapse and stochasticity is tuned in either direction with application of positive and negative current pulses. In the presence of more positive pulses, the MTJ gradually moves into low resistance state, while in the presence of more negative pulses it moves into high resistance state, as shown in Fig. 2(d). When a current pulse is applied, the probability of domain wall moving increases. The dynamic pattern recognition capabilities of the circuit are tested on 9x9 cross-bar architecture, consisting of 9 pre-neurons, 9 post-neurons and 81 synapses. In summary, here we have successfully designed and demonstrated the on-chip stochastic dynamic learning by adopting the spike time dependent plasticity as the learning algorithm. At the end of training phase, we demonstrated that all pattern synapses stochastically evolve into low resistance state (high weight) while the remaining background synapses are in high resistance state (low weight).

**References:**

[1]. Deming Zhang, Weisheng Zhao et. al, “All spin neural networks based on compound spintronic synapses and neurons,” IEEE Advances in Biomedical Circuits and Systems, vol. 10, no.4, August-2016. [2]. Otitoaleke Akinola, Jean Anne C Incorvia et.al, “Three terminal magnetic tunnel junction circuits showing spike timing-dependent plasticity,” J. Phys. D: Appl. Phys. 52 (2019) 49LT01 (11pp). [3]. Yangqi Huang, Weisheng Zhao et.al, “Magnetic-skyrmion based synaptic device,” IOP Nanotechnology, 28 08LT02, 2017 [4]. Jiang Nan, Weisheng Zhao et.al, “Efficient Magnetic Domain Nucleation and Domain Wall Motion with Voltage Control Magnetic Anisotropy Effect and Antiferromagnetic/Ferromagnetic Coupling,” IEEE TRANSACTIONS ON MAGNETICS, Vol. 55, No. 7, JULY 2019. [5]. Jialin Cai et.al, “Voltage-Controlled Spintronic Stochastic Neuron Based on a Magnetic Tunnel Junction,” Phys. Rev. Applied 11, 034015, 6 March 2019.[6]. M. Bauer, R. Alexis, G. Atwood, B. Baltar, A. Fazio, K. Frary, M. Hensel, M. Ishac, J. Javanifard, M. Landgraf, D. Leak, K. Loe, D. Mills, P. Ruby, R. Rozman, S. Sweha, S. Talreja, and K. Wojciechowski, in IEEE International Solid-State Circuits Conference Digest of Technical Papers (1995), p. 132. [7]. T. S. Jung, Y. J. Choi, K. D. Suh, B. H. Suh, J. K. Kim, Y. H. Lim, Y. N. Koh, J. W. Park, K. J. Lee, J. H. Park, K. T. Park, J. R. Kim, J. H. Yi, and H. K. Lim, IEEE J. Solid-State Circuits 31, 1575 (1996). [8]. X. H. Lou, Z. Gao, D. V. Dimitrov, and M. X. Tang, Appl. Phys. Lett. 93, 242502 (2008). [9]. N. Locatelli, V. Cros, and J. Grollier, Nat. Mater. 13, 11 (2014). [10]. F. N. Tan et.al, “High Velocity Domain wall propagation using voltage control magnetic anisotropy,” Scientific Reports, 2019) 9:7369.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/11865266d897f1c54b8d59f2476fef1b.jpg)
Fig. 1: (a) Neuromorphic circuit implementation of spike time dependent plasticity-based pattern recognition. 
The circuit consists of voltage-controlled magnetic anisotropy post neuron and stochastic 3T-MTJ synapse with domain wall driven by SOT. (b) 3T-MTJ stochastic synapse schematic with small notches. (c) Cross-bar architecture implementation of the circuit.

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Fig. 2: (a) Domain wall position in neuron for pinning ON and pinning OFF controlled by gate voltage. (b) Domain wall velocity for the pinning and non-pinning case. (c) Magnetization in the free layer MTJ synapse showing stochastic behavior with a positive current pulse. (d) MuMAG – NEGF coupled computed resistance evolution of the synapse.

 Domain Wall Motion based Neuromorphic Computing with Voltage Controlled Spin Neuron and Stochastic Magnetic Tunnel Junction Synapse

The magnetic tunnel junction (MTJ) has gained a lot of interest in neuromorphic computing owing to its tunable switching behavior. Particularly, the stochastic switching behavior of the MTJ provides an intrinsic capability to tackle computationally difficult problems in an energy efficient manner. At the architectural level, the potential associative ability of Boltzmann Machine (BM) allows it to make inference for the input patterns which is not included in the defined input set [1][2]. Motivated by the computational abilities of MTJ and associative abilities of Boltzmann Machine, in this work, we present the spin orbit torque (SOT) driven MTJ based Boltzmann Machine neural network for solving classification problems. We use a fixed learning approach to demonstrate the ability of the BM based topology. Stochastic MTJ neuron model is used in artificial neural network. The sigmoidal response of MTJ switching probability enables us to utilize the inherent stochastic device characteristic to be implemented as a stochastic unit in BM. Fig. 1(a) shows the sigmoidal switching response of the stochastic MTJ and Fig. 1(b) shows the magnetization dynamics of MTJ. We can clearly observe an SOT tunable stochastic switching behavior. The block diagram of the BM pattern classifier is shown in Fig. 2(a), where each neuron in first layer is connected to output neuron via a fixed weight. To ensure inhibitory connection, the output from each neuron acts as input to rest of the output layer. The average value of the output for all possible outcomes of neurons and for the particular case of specific output to be high (for O2 and O6) is shown in Fig. 2(b). Based on input pattern and synaptic configuration only one output has high value and a winner takes all (WTA) circuit will be able to select the desired output, signifying the proper classification problem. Finally, we conclude by discussing the applicability of these device models and architectures for various other unconventional tasks such as combinatorial optimization and probabilistic inference.

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(a): Acceptance probability vs difference function in BM (b) Stochastic switching behavior of SOT driven MTJ.

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(a): MTJ based BM architecture. (b) Mean value of output neuron layer.

Spin Orbit Torque Driven Stochastic MTJ Neuron-based Boltzmann Machine for Pattern Classification Application

Implantable drug delivery system is a prevailing approach for the drug administration [1]. The main challenge of implantable drug delivery systems is reliable implementation of a low power actuation device and a micropump with an accurate flow rate. Here we introduce a low flow rate implantable micropump fabricated using 3D printing, based on a low-power electromagnetic actuating system. We have designed the micropump structure shown in Fig. 1(a), made from polydimethylsiloxane (PDMS). The top membrane, which is 100 μm thick, was created using the spin coating. The cylindrical NdFeB magnet was placed on the membrane. The micropump structure and the membrane were bonded by 20 s of oxygen plasma. A solenoid with 2000 turns was applied as the actuator for the micropump, and it was located 2mm above the micropump as Fig. 1(b) depicts. Fig. 1(c) shows how we use the diffuser-nozzle element to achieve valveless one-direction flow. The electromagnetically actuated field could provide a periodic force on the membrane, so this top layer could deflect downward and upward. During the upward motion, negative relative pressure would be created inside the chamber and there will be more liquid drawn into the pump chamber through the inlet. On the contrary, during the membrane’s downward motion, the pressure inside the chamber rises thus more liquid exit through the nozzle than the diffuser. Therefore, a net volume of liquid would be transferred from the diffuser to the nozzle for every period [2]. Under the actuation of a varying magnetic field generated by the electromagnet, micropump can produce a flow rate of 6±0.21 μl/s, which is close to previously simulated work [3]. The fabricated micropump is shown in Fig. 1(d). In summary, we successfully designed and fabricated an implantable drug delivery system, with high precision drug release [4]. The valveless unidirectional flow is achieved by using the diffuser-nozzle structure. More studies will be performed to implement a miniaturized, fully automatic and comfortable system for daily injection applications.

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3D Printed Electromagnetics Micropump for Implantable Drug Delivery

3D Printed Electromagnetics Micropump for Implantable Drug Delivery

**Please click on the poster to see the abstract or leave your comment to the author.** 
**Session Co-Chairs:** Shouzhong Peng, *Beihang University* and Luding Wang, *Eindhoven University of Technology*

DPA: Spintronic Devices for Memory and Computing

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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For tickets to the event, please register at the following link: https://magnetism.org/registration

Please register for the MMM 2022 conference to join the event.

MMM 2022

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

**Please click on the poster to see the abstract or leave your comment to the author.** 
**Session Chair:** Yue Li, *Argonne National Laboratory*

EPB: Skyrmions and Spin-Orbitronics

**Please click on the poster to see the abstract or leave your comment to the author.** 
**Session Chair:** Lucas Caretta, *Brown University*

FPA: Spin Current and Dynamics

**Please click on the poster to see the abstract or leave your comment to the author.** 
**Session Chair:** Diana Leitao, *Eindhoven University of Technology*

OPA: Magnetic Sensors and Devices III

**Please click on the poster to see the abstract or leave your comment to the author.** 
**Session Co-Chairs:** Hendrik Ohldag, *Lawrence Berkeley National Laboratory* and Thomas Feggeler, *Lawrence Berkeley National Laboratory*

RPA: Magnetic Characterization and Imaging

GOD - Skyrmion Stabilization, Voltage Control, and Neuromorphic Applications

technical paper

The 15th Joint MMM-INTERMAG Conference (2022 Joint) 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 plenary session, and an evening session, with about 1500 presentations overall. This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

The 2022 Joint Conference will offer both in-person and prerecorded on-demand content.

For tickets to the event, please register at the following link: https://magnetism.org/register

Please register for the Joint MMM-Intermag conference to join the event.

2022 Joint MMM-INTERMAG

*Session Co-Chair: Alice Mizrahi, Unite Mixte de Physique CNRS/Thales*

*Session Co-Chair: Dedalo Sanz Hernandez, Unite Mixte de Physique CNRS/Thales*

These on-demand presentations are available to view at your leisure. 

GB: Neuromorphic Computing

INTERMAG 2021 Welcome Video



	
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Want to buy a ticket? 

Register for INTERMAG 2021 here

[![](https://assets.underline.io/uploads/markdown_image/1/image/17ce76e7960db633258024eef8d6da7f.png)](https://intermag.org/register)

Got your ticket, but still can’t access the event?

Contact us and our team will look into it.

INTERMAG 2021

**Session chairs:** Manfred Albrecht, University of Augsburg and Michal Krupinski, Polish Academy of Sciences


GPD - MRAM, Magnetic Logic and Related Devices III

**Session chairs:** Anirudh Sharma, Johns Hopkins University School of Medicine, Ravi Hadimani, Virginia Commonwealth University and Anna Guller, Macquarie University



Selma Amara

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SHORT BIO

Presentations