The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and lateral inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform lateral inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-andfire neuron that intrinsically provides lateral inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for nonvolatile logic. Single-neuron micromagnetic simulations are provided that demonstrate the ability of this neuron to implement the required leaking, integrating, and firing. These simulations are then extended to pairs of adjacent neurons to demonstrate, for the first time, lateral inhibition between neighboring artificial neurons. Finally, this intrinsic lateral inhibition is applied to a ten-neuron crossbar structure and trained to identify handwritten digits and shown via direct large-scale micromagnetic simulation for 100 digits to correctly identify the proper signal for 94% of the digits.

Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Previous proposals for spintronic artificial neurons required external stimuli to perform the leaking functionality, one of the three fundamental functions of a leaky integrate-and-fire (LIF) neuron. The use of this external magnetic field or electrical current stimulus results in either a decrease in energy efficiency or an increase in fabrication complexity. In this work, we modify the shape of previously demonstrated three-terminal magnetic tunnel junction neurons to perform the leaking operation without any external stimuli. The trapezoidal structure causes shape-based DW drift, thus intrinsically providing the leaking functionality with no hardware cost. This LIF neuron therefore promises to advance the development of spintronic neural network crossbar arrays.Index Terms-Artificial neuron, leaky integrate-and-fire (LIF) neuron, magnetic domain wall, neural network crossbar, neuromorphic computing, three-terminal magnetic tunnel junction (3T-MTJ)

Due to their nonvolatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of any external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient and the third uses shape variations of the DW track.

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Modeling computer architecture after the human brain is a promising approach to the ever-increasing demands for real-time processing of massive amounts of data. But, the building blocks of the brain, i.e. neurons and synapses, have more detailed behavior than simply how they are connected, and this behavior is central to their computing efficiency. If we want to capture the energy efficiency of such a neuromorphic architecture, we need devices themselves that can capture the behavior of the biological elements. Magnetic materials and devices have many properties similar to the brain that make them attractive candidates as artificial neurons and synapses. We will present our recent work on designing and modeling three-terminal domain wall-magnetic tunnel junctions (DW-MTJs) that can act as both leaky, integrate, and fire neurons with inherent lateral inhibition [1-2] and as synapses with online learning and spike-timing dependent plasticity behavior [3]. The DW-MTJ neuron consists of a perpendicularly magnetized wire containing a single DW and an MTJ on top of the wire, shown in Fig. a. When current is applied to the wire, the DW propagates through spin transfer torque (STT) or spin orbit torque (SOT). The MTJ defines the firing point of the neuron: when the DW moves past the junction, the MTJ resistance switches to generate a spiking current at the MTJ output terminal. We will show results of integration, firing, and inherent leaking of the device. When assembled into a crossbar array as shown in Fig. b, 1-10 digit identification is simulated by measuring which of ten neurons reach the firing point first as shown in Fig. c. Lateral inhibition (LI) is an important function in neuromorphic computing, modeled after the biological neuron behavior that a neuron prevents its less active neighbors in the same layer from firing. In most neuromorphic hardware platforms, LI is implemented with external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. We show that the DW-MTJ neuron has inherent LI via magnetic interactions. We will show results from modeling current- and field-driven DW motion in adjacent DW-MTJ neurons. Figure d shows that with 90 nm spacing between two 50 nm wide DW-MTJs, the slower device’s DW velocity is drastically reduced from 75 m/s to 20 m/s and a LI of 75% is achieved. The results are explained by current-driven DW velocity characteristics in response to external magnetic fields and are quantified by an analytical model based on the Landau-Lifshitz-Gilbert (LLG) equation. We will show results of arrays of DW-MTJs implementing a soft winner-take-all function and application of the effect to image recognition. We will furthermore present simulation results of the DW-MTJ showing synaptic behavior when the MTJ is made long. By constructing a small circuit around the device we implement spike-timing-dependent plasticity (STDP), where the current flowing through the synapse, which will determine its resistive synaptic weight, is determined by the delay between input and output neurons (Fig. e). We will show that the STDP synapses can process time-dependent input data. The results will be compared with other emerging nonvolatile devices for artificial neural networks. The work presented shows that DW-MTJs can provide a monolithic platform for neuromorphic computing circuits, where both synapses and neurons can be made from the same thin film stack, and motivate further study of magnetic devices for neuromorphic computing. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. References N. Hassan, X. Hu, L. Jiang-Wei, W. H. Brigner, O. G. Akinola, F. Garcia-Sanchez, M. Pasquale, C. H. Bennett, J. A. C. Incorvia & J. S. Friedman. Magnetic domain wall neuron with lateral inhibition. J. Appl. Phys. 124, (2018). C. Cui, O. G. Akinola, N. Hassan, H. Christopher, M. J. Marinella, J. S. Friedman & J. A. C. Incorvia. Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing. IOP Nanotechnology (2020). O. G. Akinola, X. Hu, C. H. Bennett, M. Marinella, J. S. Friedman & J. A. C. Incorvia. Three-terminal magnetic tunnel junction synapse circuits showing spike-timing-dependent plasticity. J. Phys. D. Appl. Phys. 52, 49LT01 (2019). Figure 1