Stochastic Domain Wall-Magnetic Tunnel Junction Artificial Neurons for Noise-Resilient Spiking Neural Networks

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make 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 (MW) DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire (LIF) 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.Comment: 10 pages, 4 figure

Tunnel magnetoresistance in scandium nitride magnetic tunnel junctions using first principles

The magnetic tunnel junction is a cornerstone of spintronic devices and circuits, providing the main way to convert between magnetic and electrical information. In state-of-the-art magnetic tunnel junctions, magnesium oxide is used as the tunnel barrier between magnetic electrodes, providing 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 causes challenges of device-to-device variability and tunnel barrier degradation under high current. Here, we study using first principles narrower-bandgap scandium nitride tunneling properties and transport in magnetic tunnel junctions in comparison to magnesium oxide. These simulations demonstrate a high tunnel magnetoresistance in Fe/ScN/Fe MTJs via {\Delta}_1 and {\Delta}_2' symmetry filtering with low wavefunction decay rates, allowing a low resistance-area product. The results show that scandium nitride could be a new tunnel barrier material for magnetic tunnel junction devices to overcome variability and current-injection challenges

Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification

Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing. In this paper, AP-CNFETs are used to design a mixed-signal machine learning (ML) classifier. The classifier is designed in SPICE with feature size of 15 nm and operates at 250 MHz. The system is demonstrated based on MNIST digit dataset, yielding 90% accuracy and no accuracy degradation as compared with the classification of this dataset in Python. The system also exhibits lower power consumption and smaller physical size as compared with the state-of-the-art CMOS and memristor based mixed-signal classifiers

Tunnel magnetoresistance in scandium nitride magnetic tunnel junctions using first principles

The magnetic tunnel junction is a cornerstone of spintronic devices and circuits, providing the main way to convert between magnetic and electrical information. In state-of-the-art magnetic tunnel junctions, magnesium oxide is used as the tunnel barrier between magnetic electrodes, providing 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 causes challenges of device-to-device variability and tunnel barrier degradation under high current. Here, we study using first principles narrower-bandgap scandium nitride tunneling properties and transport in magnetic tunnel junctions in comparison to magnesium oxide. These simulations demonstrate a high tunnel magnetoresistance in Fe/ScN/Fe MTJs via Δ1 and Δ2′ symmetry filtering with low wavefunction decay rates, allowing a low resistance-area product. The results show that scandium nitride could be a new tunnel barrier material for magnetic tunnel junction devices to overcome variability and current-injection challenges.The authors acknowledge computing resources from the Texas Advanced Computing Center (TACC) at the University of Texas at Austin (http://www.tacc.utexas.edu), funding and discussions from Sandia National Laboratories, and funding from the Center for Dynamics and Control of Materials (CDCM) supported by the National Science Foundation under NSF Award Number DMR-1720595.Center for Dynamics and Control of Material

Low voltage local strain enhanced switching of magnetic tunnel junctions

Strain-controlled modulation of the magnetic switching behavior in magnetic tunnel junctions (MTJs) could provide the energy efficiency needed to accelerate the use of MTJs in memory, logic, and neuromorphic computing, as well as an additional way to tune MTJ properties for these applications. State-of-the-art CoFeB-MgO based MTJs still require too high voltages to alter their magnetic switching behavior with strain. In this study, we demonstrate strain-enhanced field switching of nanoscale MTJs through electric field control via voltage applied across local gates. The results show that record-low voltage down to 200 mV can be used to control the switching field of the MTJ through enhancing the magnetic anisotropy, and that tunnel magnetoresistance is linearly enhanced with voltage through straining the crystal structure of the tunnel barrier. These findings underscore the potential of electric field manipulation and strain engineering as effective strategies for tailoring the properties and functionality of nanoscale MTJs

Magnetic Tunnel Junction Random Number Generators Applied to Dynamically Tuned Probability Trees Driven by Spin Orbit Torque

Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNG) can consume orders of magnitude less energy per bit than CMOS pseudo-RNG. Here, we numerically investigate with a macrospin Landau-Lifshitz-Gilbert equation solver the use of pMTJs driven by spin-orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called 'coinflips', via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval 0,255 from an exponential distribution based on p-value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a 'bucket' to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, iwth diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNG, showing faster bit generation and significantly lower energy use.Comment: 10 pages, 8 figures, 2 table