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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.

Memristive switches are able to act as both storage and computing elements, which make them an excellent candidate for beyond-CMOS computing. In this paper, multi-input memristive switch logic is proposed, which enables the function X OR (Y NOR Z) to be performed in a single-step with three memristive switches. This ORNOR logic gate increases the capabilities of memristive switches, improving the overall system efficiency of a memristive switch-based computing architecture. Additionally, a computing system architecture and clocking scheme are proposed to further utilize memristive switching for computation. The system architecture is based on a design where multiple computational function blocks are interconnected and controlled by a master clock that synchronizes system data processing and transfer. The clocking steps to perform a full adder with the ORNOR gate are presented along with simulation results using a physics-based model. The full adder function block is integrated into the system architecture to realize a 64-bit full adder, which is also demonstrated through simulation.

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.

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)

Spintronic three-terminal magnetic-tunnel-junction (3T-MTJ) devices have gained considerable interest in the field of neuromorphic computing. Previously, these devices required external circuitry to implement the leaking functionality that leaky integrate-and-fire (LIF) neurons should display. However, the use of external circuitry results in decreased device efficiency. We previously demonstrated lateral inhibition with a 3T-MTJ neuron that intrinsically performs the leaking, integrating, and firing functions; however, it required the fabrication of a complex multilayer structure. In this paper, we introduce an anisotropy gradient to implement a single-layer intrinsically leaking 3T-MTJ LIF neuron without the use of any external circuitry. This provides the leaking functionality with no hardware cost and reduced fabrication complexity, which increases the device, circuit, system, and cost efficiency. INDEX TERMS Artificial neuron, leaky integrate-and-fire (LIF) neuron, magnetic domain wall (DW), neural network crossbar, neuromorphic computing, three-terminal magnetic tunnel junction (3T-MTJ).

We present a numerical method to accurately model the electro-optic interaction in anisotropic materials. Specifically, we combine a full-vectorial finite-difference optical mode solver with a radio-frequency solver to analyze the overlap between optical modes and applied electric field. This technique enables a comprehensive understanding on how electro-optic effects modify individual elements in the permittivity tensor of a material. We demonstrate the interest of this approach by designing a modulator that leverages the Pockels effect in a hybrid silicon-BaTiO₃ slot waveguide. Optimized optical confinement in the active BaTiO₃ layer as well as design of travelling-wave index-matched electrodes is presented. Most importantly, we show that the overall electro-optic modulation is largely governed by off-diagonal elements in the permittivity tensor. As most of active electro-optic materials are anisotropic, this method paves the way to better understand the physics of electro-optic effects and to improve optical modulators.

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