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.

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.

Magnetic skyrmions are exciting candidates for energy-efficient computing due to their nonvolatility, detectability, and mobility. A recent proposal within the paradigm of reversible computing enables large-scale circuits composed of directly cascaded skyrmion logic gates, but it is limited by the manufacturing difficulty and energy costs associated with the use of notches for skyrmion synchronization. To overcome these challenges, we, therefore, propose a skyrmion logic synchronized via modulation of voltage-controlled magnetic anisotropy (VCMA). In addition to demonstrating the principle of VCMA synchronization through micromagnetic simulations, we also quantify the impacts of current density, skyrmion velocity, and anisotropy barrier height on skyrmion motion. Further micromagnetic results demonstrate the feasibility of cascaded logic circuits in which VCMA synchronizers enable clocking and pipelining, illustrating a feasible pathway toward energy-efficient large-scale computing systems based on magnetic skyrmions.

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 1 . Magnetic tunnel junction (MTJ) memory elements can be used for computation by manipulating a domain wall (DW), a transition region between magnetic domains. Three leading device types that use MTJs and DWs for in-memory computing are majority logic 2-4 , mLogic 5-9 , and DW-MTJs 10-14 . But, these devices have suffered from challenges: spin transfer torque (STT) switching of a DW requires high current, and the multiple etch steps needed to create an MTJ pillar on top of a DW track has led to reduced tunnel magnetoresistance (TMR) 15-16 . These issues have limited experimental study of devices and circuits. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction (DW-MTJ) in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy (PMA), spin-orbit torque (SOT) switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 164%, resistance-area product RA = 31 𝛀 − 𝝁𝒎 𝟐 , close to the RA of the unpatterned film, and lower switching current density compared to using spin transfer torque. A two-device circuit shows bit propagation between devices. Device initialization variation in switching voltage is shown to be curtailed to 7% by controlling the DW initial position, which we show corresponds to 96% accuracy in a DW-MTJ full adder simulation. These results make strides in using MTJs and DWs for in-memory and neuromorphic computing applications.Computing today faces walls when processing data-intensive and unstructured tasks. Memory access in modern computers can dominate as much as 96% of computing time 17 . SRAM idle leakage can consume over 20% of the total power of a computation 18,19 . For internet of things

Lateral inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms lateral inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wallmagnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be inherently inhibitory. Without peripheral circuitry, lateral inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the lateral inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak inhibition only in very closely-spaced devices. This work approaches these problems by modeling current-and field-driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of lateral inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to external magnetic field and quantified by an analytical model. Finally, the dependence of lateral inhibition strength on device parameters is investigated. This provides a guideline for the optimization of lateral inhibition implementation in DW-MTJ neurons. With strong lateral inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.

Little is known about the health status of the 7.3 million Americans who enrolled in insurance plans through the Marketplaces established by the Affordable Care Act in 2014. Medication use may provide an early indicator of the health needs and access to care among Marketplace enrollees. We used data from January-September 2014 on more than one million Marketplace enrollees from Express Scripts, the largest pharmacy benefit management company in the United States. We compared the characteristics and medication use between early and late Marketplace enrollees and between all Marketplace enrollees and enrollees with employer-sponsored insurance. Among Marketplace enrollees, we found that those who enrolled earlier (October 2013-February 2014) were older and used more medication than later enrollees. Marketplace enrollees, as a whole, had lower average drug spending and were less likely to use most medication classes than the employer-sponsored comparison group. However, Marketplace enrollees were more likely to use medicines for hepatitis C and particularly for HIV.

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