Research Highlights

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Photothermal therapy (PTT) and magnetic hyperthermia therapy (MHT) using 2D nanomaterials (2DnMat) have recently emerged as promising alternative treatments for cancer and bacterial infections, both important global health challenges. The present review intends to provide not only a comprehensive overview, but also an integrative approach of the state-of-the-art knowledge on 2DnMat for PTT and MHT of cancer and infections. High surface area, high extinction coefficient in near-infra-red (NIR) region, responsiveness to external stimuli like magnetic fields, and the endless possibilities of surface functionalization, make 2DnMat ideal platforms for PTT and MHT. Most of these materials are biocompatible with mammalian cells, presenting some cytotoxicity against bacteria. However, each material must be comprehensively characterized physiochemically and biologically, since small variations can have significant biological impact. Highly efficient and selective in vitro and in vivo PTTs for the treatment of cancer and infections are reported, using a wide range of 2DnMat concentrations and incubation times. MHT is described to be more effective against bacterial infections than against cancer therapy. Despite the promising results attained, some challenges remain, such as improving 2DnMat conjugation with drugs, understanding their in vivo biodegradation, and refining the evaluation criteria to measure PTT or MHT effects.

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Domain-wall magnetic tunnel junctions are a new spintronic device family that may be exploited in resilient edge logic processors or neuromorphic edge accelerators in the future. Here, domain-wall magnetic tunnel junction logic devices were exposed to large total ionizing doses, heavy ion displacement damage, or both. The parts demonstrated complete resilience to the ionizing radiation, but ion-irradiated parts followed a similar degradation curve to previously tested tunnel junction parts in response to heavy ion irradiation. Microscopy and spectroscopy methods confirm significant damage in some devices.

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With the rise in in-memory computing architectures to reduce the compute-memory bottleneck, a new bottleneck is present between analog and digital conversion. Analog contentaddressable memories (ACAM) are being recently studied for in-memory computing to efficiently convert between analog and digital signals. Magnetic memory elements such as magnetic tunnel junctions (MTJs) could be useful for ACAM due to their low read/write energy and high endurance, but MTJs are usually restricted to digital values. The spin orbit torque-driven domain wall-magnetic tunnel junction (DW-MTJ) has been recently shown to have multi-bit function. Here, an ACAM circuit is studied that uses two domain wall-magnetic tunnel junctions (DW-MTJs) as the analog storage elements. Prototype DW-MTJ data is input into the magnetic ACAM (MACAM) circuit simulation, showing ternary CAM function. Device-circuit co-design is carried out, showing that 8-10 weight bits are achievable, and that designing asymmetrical spacing of the available DW positions in the device leads to evenly spaced ACAM search bounds. Analyzing available spin orbit torque materials shows platinum provides the largest MACAM search bound while still allowing spin orbit torque domain wall motion, and that the circuit is optimized with minimized MTJ resistance, minimized spin orbit torque material resistance, and maximized tunnel magnetoresistance. These results show the feasibility of using DW-MTJs for MACAM and provide design parameters.

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Mechanical strain provides a knob for controlling the magnetization of the magnetostrictive-free layer of magnetic tunnel junctions (MTJs), with many applications for energy-efcient memory and computing. This requires integrating materials with high magnetostriction coefcient into MTJs, while still preserving the CoFeB-MgO tunnel barrier for high tunnel magnetoresistance (TMR). One way to accomplish this is to replace the CoFeB free layer of the MTJ with an exchange-coupled bilayer of CoFeB and a highly magnetostrictive ferromagnet like Galfenol (FeGa). Here, FeGa, a thermally stable magnetostrictive material, is integrated into CoFeB-based MTJs. We show that engineering a thin layer of CoFeB and FeGa provides a means of controlling the magnetic properties and switching feld in FeGa-based MTJs, and that the exchange-coupled FeGa-CoFeB layer can be used as both a free layer and a fxed layer in the MTJ stack with TMR as high as 100%.

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Ambipolar dual-gate transistors based on low-dimensional materials, such as graphene, carbon nanotubes, black phosphorus, and certain transition metal dichalcogenides (TMDs), enable reconfigurable logic circuits with a suppressed off-state current. These circuits achieve the same logical output as complementary metal–oxide semiconductor (CMOS) with fewer transistors and offer greater flexibility in design. The primary challenge lies in the cascadability and power consumption of these logic gates with static CMOS-like connections. In this article, high-performance ambipolar dual-gate transistors based on tungsten diselenide (WSe2) are fabricated. A high on–off ratio of 108 and 106, a low off-state current of 100 to 300 fA, a negligible hysteresis, and an ideal subthreshold swing of 62 and 63 mV/dec are measured in the p- and n-type transport, respectively. We demonstrate cascadable and cascaded logic gates using ambipolar TMD transistors with minimal static power consumption, including inverters, XOR, NAND, NOR, and buffers made by cascaded inverters. A thorough study of both the control gate and the polarity gate behavior is conducted. The noise margin of the logic gates is measured and analyzed. The large noise margin enables the implementation of VT-drop circuits, a type of logic with reduced transistor number and simplified circuit design. Finally, the speed performance of the VT-drop and other circuits built by dual-gate devices is qualitatively analyzed. This work makes advancements in the field of ambipolar dual-gate TMD transistors, showing their potential for low-power, high-speed, and more flexible logic circuits.

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The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring advantages of scalability and energy efficiency. Domain wall (DW)-based magnetic tunnel junction (MTJ) devices are well suited for probabilistic neural networks given their intrinsic integrate-and-fire behavior with tunable stochasticity. Here, we present a scaled DW-MTJ neuron with voltage-dependent firing probability. The measured behavior was used to simulate a SNN that attains accuracy during learning compared to an equivalent, but more complicated, multi-weight DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire device. However, during inference, the binary DW-MTJ neuron outperformed the other devices after Gaussian noise was introduced to the Fashion-MNIST classification task. This work shows that DW-MTJ devices can be used to construct noise-resilient networks suitable for neuromorphic computing on the edge.

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The domain wall-magnetic tunnel junction (DW-MTJ) is a versatile device that can simultaneously store data and perform computations. These three-terminal devices are promising for digital logic due to their nonvolatility, low-energy operation, and radiation hardness. Here, we augment the DW-MTJ logic gate with voltage-controlled magnetic anisotropy (VCMA) to improve the reliability of logical concatenation in the presence of realistic process variations. VCMA creates potential wells that allow for reliable and repeatable localization of domain walls (DWs). The DW-MTJ logic gate supports different fanouts, allowing for multiple inputs and outputs for a single device without affecting the area. We simulate a systolic array of DW-MTJ multiply-accumulate (MAC) units with 4-bit and 8-bit precision, which uses the nonvolatility of DW-MTJ logic gates to enable fine-grained pipelining and high parallelism. The DW-MTJ systolic array provides comparable throughput and efficiency to state-of-the-art CMOS systolic arrays while being radiation-hard. These results improve the feasibility of using DW-based processors, especially for extreme environment applications such as space.

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In this study, micromagnetic simulations of a magnetic skyrmion reshuffling chamber for probabilistic computing applications are performed.The skyrmion shuffling chamber is modeled with a custom current density masking technique to capture current density variation, grain boundary variations, and anisotropy changes. The results show that the skyrmion oscillatory dynamics contribute to the system’s stochasticity, allowing uncorrelated signals to be achieved with a single chamber. Our findings indicate that uncorrelated signals are generally achieved at all temperatures simulated, with the skyrmion diameter playing a role in the resulting stochasticity. Furthermore, we find that local temperature control has the benefit of not affecting the overall skyrmion diameter, while still perturbing the skyrmion trajectory. The results from varying chamber size, global temperature, and local temperature are analyzed using Pearson correlation coefficient and p-value. This research contributes to the development of tunable probabilistic computing devices and artificial synapses using magnetic skyrmions.

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Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. Device candidates for this application need to produce highly random ‘‘coinflips’’ while also having tunable biasing of the coin. The magnetic tunnel junction (MTJ) has been studied as an RNG due to its thermally-driven magnetization dynamics, often using spin transfer torque (STT) current amplitude to control the random switching of the MTJ free layer (FL) magnetization, here called the stochastic write method. There are additional knobs to control the MTJ- RNG, including voltage-controlled magnetic anisotropy (VCMA) and spin orbit torque (SOT), and there is a need to systematically study and compare these methods. We build an analytical model of the MTJ to characterize using VCMA and SOT to generate random bit streams. The results show that both methods produce high-quality, uniformly distributed bitstreams. Biasing the bitstreams using either STT current or an applied magnetic field shows a sigmoidal distribution versus bias amplitude for both VCMA and SOT, compared to less sigmoidal for stochastic write. The energy consumption per sample is calculated to be 0.1 pJ (SOT), 1 pJ (stochastic write), and 20 pJ (VCMA), revealing the potential energy benefit of using SOT and showing using VCMA may require higher damping materials. The generated bitstreams are then applied to two tasks: generating an arbitrary probability distribution and using the MTJ-RNGs as stochastic neurons to perform simulated annealing, where both VCMA and SOT methods show the ability to effectively minimize the system energy with a small delay and low energy. These results show the flexibility of the MTJ as a true RNG and elucidate design parameters for optimizing the device operation for applications.

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The safety of nano-enabled devices must be assessed throughout the device lifecycle. Landfills are the end-of-life repository for functional devices; however, the dynamism of leachate age and complexity of leachate chemistry make material safety assessments challenging. This study evaluates the transformation and effects of water treatment membranes and transistors enabled with MoS2—a transition metal dichalcogenide (TMD) used in electronic transistors and futuristic wearable electronics—in complex landfill conditions. Device decay and MoS2 transformation are studied with a suite of characterization tools. The effects of MoS2 colloids on microbial diversity in young and mature landfill leachates are investigated. Results indicate that MoS2-enabled membranes and transistors become attached to residues from the leachate but do not undergo major chemical decay. Furthermore, complex environments like leachates are robust, resisting microbiome changes upon exposure to MoS2. The results obtained under the specific study conditions indicate that disposal of MoS2-enabled devices in landfills does not induce significant deleterious effects on the landfill in the absence of photo-transformation (as is the case in covered landfills). However, further studies are needed to assess whether a high concentration of MoS2, a likely result of its accumulation in the landfill over time, has the potential to substantially change the leachate microbiome.

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The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain’s ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.

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Bayesian neural networks (BNNs) combine the generalizability of deep neural networks (DNNs) with a rigorous quantification of predictive uncertainty, which mitigates overfitting and makes them valuable for high-reliability or safety-critical applications. However, the probabilistic nature of BNNs makes them more computationally intensive on digital hardware and so far, less directly amenable to acceleration by analog in-memory computing as compared to DNNs. This work exploits a novel spintronic bit cell that efficiently and compactly implements Gaussian-distributed BNN values. Specifically, the bit cell combines a tunable stochastic magnetic tunnel junction (MTJ) encoding the trained standard deviation and a multi-bit domain-wall MTJ device independently encoding the trained mean. The two devices can be integrated within the same array, enabling highly efficient, fully analog, probabilistic matrix-vector multiplications. We use micromagnetics simulations as the basis of a system-level model of the spintronic BNN accelerator, demonstrating that our design yields accurate, well-calibrated uncertainty estimates for both classification and regression problems and matches software BNN performance. This result paves the way to spintronic in-memory computing systems implementing trusted neural networks at a modest energy budget.

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Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain’s intellectual abilities. An impressive ability of the brain is its adaptive intelligence, which allows the brain to regulate its functions “on the fly” to cope with myriad and ever-changing situations. In particular, the brain displays three adaptive and advanced intelligence abilities of context-awareness, cross frequency coupling, and feature binding. To mimic these adaptive cognitive abilities, we design and simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion lattice, thereby modulating the neuron’s state, its dynamics and its transfer function “on the fly.” This adaptive neuron is used to demonstrate the three cognitive abilities, of which context-awareness and cross-frequency coupling have not been previously realized in hardware neurons. Additionally, the neuron is used to construct an adaptive artificial neural network (ANN) and perform context-aware diagnosis of breast cancer. Simulations show that the adaptive ANN diagnoses cancer with higher accuracy while learning faster and using a more compact and energy-efficient network than a nonadaptive ANN. The work further describes how hardware-based adaptive neurons can mitigate several critical challenges facing contemporary ANNs. Modern ANNs require large amounts of training data, energy, and chip area, and are highly task-specific; conversely, hardware-based ANNs built with adaptive neurons show faster learning, compact architectures, energy-efficiency, fault-tolerance, and can lead to the realization of broader artificial intelligence.

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In neuromorphic computing, artificial synapses provide a multi-weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin-orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application-specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion-MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR-100 image recognition, the rectangular magnetic synapse achieves near-ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.

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Heavy metal-based two-dimensional van der Waals materials have a large, coupled spin and valley Hall effect (SVHE) that has potential use in spintronics and valleytronics. Optical measurements of the SVHE have largely been performed below 30 K, and understanding of the SVHE-induced spin/valley polarizations that can be electrically generated is limited. Here, we study the SVHE in monolayer p-type tungsten diselenide (WSe2). Kerr rotation (KR) measurements show the spatial distribution of the SVHE at different temperatures, its persistence up to 160 K, and that it can be electrically modulated via gate and drain bias. A spin/valley drift and diffusion model together with a reflection measurement and a four-port electrical measurement is used to interpret the KR data. A lower-bound spin/valley lifetime is predicted to be of 40 ns and a mean free path of 240 nm below 90 K, 2 orders of magnitude higher than a previous work that uses similar methods. The spin/valley polarization on the edge is calculated to be ∼4% at 45 K. These results are important steps toward practical use of the SVHE.

4 figures are shown, lettered a-d. From left to right, figure a shows two fingers holding a transparent object in front of the UT Tower in the background, figure b shows yellow planes letters “S,D,G” and 2 large teal planes titles “Nation” and “PMMA/Graphene,” figure c shows two images demonstrating a low and high conductance state, figure d shows a graph which measures time on the x-axis and Conductance on the Y-axis.

CMOS-based computing systems that employ the von Neumann architecture
are relatively limited when it comes to parallel data storage and processing. In
contrast, the human brain is a living computational signal processing unit that
operates with extreme parallelism and energy efficiency. Although numerous
neuromorphic electronic devices have emerged in the last decade, most of
them are rigid or contain materials that are toxic to biological systems. In this
work, we report on biocompatible bilayer graphene-based artificial synaptic
transistors (BLAST) capable of mimicking synaptic behavior. The BLAST

devices leverage a dry ion-selective membrane, enabling long-term potentia-
tion, with ~50 aJ/μm2 switching energy efficiency, at least an order of magni-
tude lower than previous reports on two-dimensional material-based artificial

synapses. The devices show unique metaplasticity, a useful feature for gen-
eralizable deep neural networks, and we demonstrate that metaplastic BLASTs

outperform ideal linear synapses in classic image classification tasks. With
switching energy well below the 1 fJ energy estimated per biological synapse,
the proposed devices are powerful candidates for bio-interfaced online
learning, bridging the gap between artificial and biological neural networks.

blue nuclei object surrounded by purple particles

The state-of-the-art magnetic tunnel junction, a cornerstone of spintronic devices and circuits, uses a magnesium oxide tunnel barrier that provides a uniquely large tunnel magnetoresistance at room temperature. However, the wide bandgap and band alignment of magnesium oxide-iron systems increases the resistance-area product and creates variability and breakdown challenges. Here, the authors study using first principles narrower-bandgap scandium nitride (ScN) transport properties in magnetoresistive junctions in comparison to magnesium oxide. The results show a high magnetoresistance in Fe/ScN/Fe via Δ1and Δ2′ symmetry filtering with low wave function decay rates, suggesting scandium nitride could be a new barrier material for spintronic devices.

Four images displayed in black and white, textured objects are pointed out with yellow arrows. From top to bottom on the left, the images read “(a) 1um, (c) 2um.” From top to bottom on the right, the images read “(b) 1um, (d) 1um”

MXenes are a large class of materials that are chemically exfoliated from metal–aluminum–carbon (MAX) bulk crystals into low-dimensional sheets. While many MXenes have been theoretically predicted, the careful balance required in the exfoliation between breaking the inter-layer bonds without damaging the intra-layer bonds of the sheets has limited synthesis and experimental study. Here, we developed the synthesis of Cr2C from its parent Cr2AlC MAX phase and showed the etching is optimized using sodium fuoride and hydrogen chloride with a modifed minimally intensive layer delamination (mMILD) method in a cold environment of 9 °C. We further optimized the intercalation and delamination using sonication and washing methods. The resulting Cr2C crystal structure was characterized. These results open up Cr2C to experimental study, including of its predicted emergent magnetic properties, and develop guidelines for synthesizing new MXene materials.

Three similar images are displayed in a row, the top of each having colorful pixels and the bottom having a graph with colors representing those pixels. Each graph reads on the y-axis “Normalized counts, 10^4, 10^5” and on the x-axis “Energy loss (eV), 500, 1000, 1500, 2000.” Each graph labels the top right corner as “Ta-M5, 4” and the plotted lines are labeled from left to right as “O-K, Fe-L3,2, Co-L3,2.” On the left, from left to right, the pixels are colored red, green, blue, magenta, and yellow, reading “(a) Ta No Does, Free CoFeB, MgO, Ref CoFeB 2nm, Cap.” In the middle, from left to right, the pixels are colored red, green, blue, yellow, magenta, and yellow, reading “(c)” on top left corner, “5x10^12 ions/cm^2” on the bottom left, and “2nm” on the bottom right. On the right pixel image, it is colored red, green, blue, green, magenta, and yellow. Top left corner reads “(e)”, bottom left “1x10^14 ions/cm^2” and the bottom right “2nm.” Bottom graphs from left to right are labeled on the top left corner as “(b), (d), (f).”

We study the impact of irradiation on magnetic tunnel junction (MTJ) films with perpendicular magnetic anisotropy (PMA) and spin-orbit torque (SOT) switching using magneto-optical Kerr effect and transmission electron microscopy. Our results show that the thin-film stack is robust to gamma ionizing dose up to 1 Mrad(Si) and Ta 1+ ion irradiation fluences up to 10 12 ions/cm 2 , showing SOT PMA MTJs are radiation-hard. But, at very high Ta 1+ ion irradiation between 10 12 and 10 14 ions/cm 2 , reduced coercivity and eventually greatly reduced PMA are observed, corresponding with an increase in intermixing of the CoFeB-MgO layers, particularly at the lower CoFeB-MgO interface. These results agree with displacement damage modeling that predicts higher damage in the layers closer to the bottom heavy metal and substrate. Compared to spin transfer torque and in-plane anisotropy MTJs, needing a top-pinned stack, a thicker heavy metal layer, and perpendicular anisotropy that is pinned out of plane by interfaces, all could make SOT PMA MTJs more susceptible to damage at high doses.

5 graphs are displayed, 4 square ones and one rectangle one below the 4. Top right graph displays a box of clothing above it, the top left graph displays a box of numbers above it. All graphs are different, but measure Epochs on the x-axis and Fashion MINIST accuracy on the y-axis.

Inspired by the parallelism and efficiency of the brain, several candidates for artificial synapse devices have been developed for neuromorphic computing, yet a nonlinear and asymmetric synaptic response curve precludes their use for backpropagation, the foundation of modern supervised learning. Spintronic devices—which benefit from high endurance, low power consumption, low latency, and CMOS compatibility—are a promising technology for memory, and domain-wall magnetic tunnel junction (DW-MTJ) devices have been shown to implement synaptic functions such as long-term potentiation and spike-timing dependent plasticity. In this work, we propose a notched DW-MTJ synapse as a candidate for supervised learning. Using micromagnetic simulations at room temperature, we show that notched synapses ensure the non-volatility of the synaptic weight and allow for highly linear, symmetric, and reproducible weight updates using either spin transfer torque (STT) or spin–orbit torque (SOT) mechanisms of DW propagation. We use lookup tables constructed from micromagnetics simulations to model the training of neural networks built with DW-MTJ synapses on both the MNIST and Fashion-MNIST image classification tasks. Accounting for thermal noise and realistic process variations, the DW-MTJ devices achieve classification accuracy close to ideal floating-point updates using both STT and SOT devices at room temperature and at 400 K. Our work establishes the basis for a magnetic artificial synapse that can eventually lead to hardware neural networks with fully spintronic matrix operations implementing machine learning.

Yellow circuits, labeled from left to right, read “IN, OUT, CLK.” The middle has a gray, blue, and red colored object, the blue part is labeled as “relaxed” and the red as “Edgy.” The Z,Y, and X axis are shown at the bottom right corner. The white line separating the blue and red colors is labeled as “Domain Wall.” A blue circle on top of the red color, underneath the yellow circuit “OUT” is labeled as “Magnetic Tunnel Junction.”

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this letter, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

Left image shows yellow circuits meet in the middle of a grey object. Labeled from left to right, the 6 circuits read “Oe2, (a) IN, Oe1, 4P, OUT, CLK.” The middle image shows the same 6 circuits from birds eye view in black and white, labelling the middle object as “DW track” where it meets the far right circuit, “MTJ” where the top middle circuit meets the middle, and “w” for the width of the middle object. The Right image shows the exact same image as the middle, but with no labels.

There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction memory elements can be used for computation by manipulating a domain wall, a transition region between magnetic domains, but the experimental study of such devices has been limited by high current densities and low tunnel magnetoresistance. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy, spin–orbit torque switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 171% and average resistance-area product RA = 29 𝛺𝜇m2Ω μm2, close to the RA of the unpatterned film. Device initialization variation in switching voltage is shown to be curtailed to 7%–10% by controlling the domain wall initial position, which we show corresponds to 90%–96% accuracy in a domain wall-magnetic tunnel junction full adder simulation. Repeatability of writing and resetting the device is shown. A circuit shows an inverter operation between two devices, showing that a voltage window is large enough, compared to the variation noise, to repeatably operate a domain wall-magnetic tunnel junction circuit. These results make strides in using magnetic tunnel junctions and domain walls for in-memory and neuromorphic computing applications.

The bottom of this image is labeled as “(b)” and displays 2 long rectangular bars, with one end being red and one being blue. The left bar is labeled “Neuron I” and the right bar is labeled “Neuron N” which has black arrows curving into the left bar. A column shows red on the top, white in the middle, and blue at the bottom, labeled from top to bottom as “+1, 0, -1.” The left side of the column reads “Mz/Ms.” The top of the image is labeled as “(a) and has a long 2d rectangle displaying in, out, and CLK points of a circuit/button. Left side is colored red, right is blue. The “IN” is on the far left, “CLK” on the far right, and “OUT” right before the “CLK.” The middle fades to white as colors transition, labeled as “Vdw.” On top of that, there is a key for the buttons/colors used in this figure. Reading from top to bottom, “Contact, Tunnel Barrier, Frozen Spin, Ferromahnet magnetization.” From top to bottom, the key is a yellow rectangle, grey rectangle, tan up and down arrows, red and blue up and down arrows. The Y, Z, and X Axis are labeled at the bottom right corner.

There have been recent efforts towards the development of biologically-inspired neuromorphic devices and architecture. Here, we show a synapse circuit that is designed to perform spike-timing-dependent plasticity which works with the leaky, integrate, and fire neuron in a neuromorphic computing architecture. The circuit consists of a three-terminal magnetic tunnel junction with a mobile domain wall between two low-pass filters and has been modeled in SPICE. The results show that the current flowing through the synapse is highly correlated to the timing delay between the pre-synaptic and post-synaptic neurons. Using micromagnetic simulations, we show that introducing notches along the length of the domain wall track pins the domain wall at each successive notch to properly respond to the timing between the input and output current pulses of the circuit, producing a multi-state resistance representing synaptic weights. We show in SPICE that a notch-free ideal magnetic device also shows spike-timing dependent plasticity in response to the circuit current. This work is key progress towards making more bio-realistic artificial synapses with multiple weights, which can be trained online with a promise of CMOS compatibility and energy efficiency.

There have been recent efforts towards the development of biologically-inspired neuromorphic devices and architecture. Here, we show a synapse circuit that is designed to perform spike-timing-dependent plasticity which works with the leaky, integrate, and fire neuron in a neuromorphic computing architecture. The circuit consists of a three-terminal magnetic tunnel junction with a mobile domain wall between two low-pass filters and has been modeled in SPICE. The results show that the current flowing through the synapse is highly correlated to the timing delay between the pre-synaptic and post-synaptic neurons. Using micromagnetic simulations, we show that introducing notches along the length of the domain wall track pins the domain wall at each successive notch to properly respond to the timing between the input and output current pulses of the circuit, producing a multi-state resistance representing synaptic weights. We show in SPICE that a notch-free ideal magnetic device also shows spike-timing dependent plasticity in response to the circuit current. This work is key progress towards making more bio-realistic artificial synapses with multiple weights, which can be trained online with a promise of CMOS compatibility and energy efficiency.