Magnetic skyrmion, a nanosized spin texture with topological property, has become an area of significant interest due to the scientific insight that it can provide and also its potential impact on applications such as ultra-low-energy and ultra-high-density logic gates. In the quest for the reconfiguration of single logic device and the implementation of the complete logic functions, a novel reconfigurable skyrmion logic (RSL) is proposed and verified by micromagnetic simulations. Logic functions including AND, OR, NOT, NAND, NOR, XOR, and XNOR are implemented in the ferromagnetic (FM) nanotrack by virtue of various effects including spin orbit torque, skyrmion Hall effect, skyrmion-edge repulsions, and skyrmion-skyrmion collision. Different logic functions can be selected in an RSL by applying voltage to specific region(s) of the device, changing the local anisotropy energy of FM film. Material properties and geometrical scaling studies suggest RSL gates fit for energy-efficient computing as well as provide the guidelines for the design and optimization of this new logic family.

Memristors, demonstrated by solid-state devices with continuously tunable resistance, [1][2][3][4][5][6][7] have emerged as a new paradigm for self-adaptive networks that require synapse-like functions (artificial synapse, for example). Spin-based memristors offer advantages over other types of memristors because of their significant endurance and high energy efficiency. [8,9] Yet, it remains a challenge to build dense and functional spintronic memristors with structures and materials that are compatible with existing ferromagnetic devices. [10] Here, a memristive device based upon Ta/CoFeB/MgO heterostructures is demonstrated, which are commonly used in out-of-plane magnetized magnetic tunnel junctions. [11] To achieve the memristive function, a domain wall (DW) is driven back and forth in a continuous manner in the CoFeB layer by applying in-plane positive or negative current pulses along the Ta layer, utilizing the spin-orbit torque (SOT) that the current exerts on the CoFeB magnetization. [12][13][14][15][16][17] Hence, Memristors, demonstrated by solid-state devices with continuously tunable resistance, have emerged as a new paradigm for self-adaptive networks that require synapse-like functions (artificial synapse, for example). Spin-based memristors offer advantages over other types of memristors because of their significant endurance and high energy efficiency. Yet it remains a challenge to build dense and functional spintronic memristors with structures and materials that are compatible with existing ferromagnetic devices. Here, a memristive device based upon Ta/CoFeB/MgO heterostructures is demonstrated, which are commonly used in out-of-plane magnetized magnetic tunnel junctions (MTJ). To achieve the memristive function, a domain wall (DW) is driven back and forth in a continuous manner in the CoFeB layer by applying in-plane positive or negative current pulses along the Ta layer, utilizing the spin-orbit torque (SOT) that the current exerts on the CoFeB magnetization. Hence, the magnetization and consequently the anomalous Hall effect (AHE) resistance are modulated in an analog manner, being controlled by the pulsed current characteristics including amplitude, duration, and repetition number. The quasi-continuous AHE resistance variation is explained by the SOT-induced DW creep motion. These results pave the way for developing SOT-based energy-efficient neuromorphic systems.

Three-dimensional (3-D) nano-electro-mechanical (NEM) switches (relays) are proposed to reduce the die area and power consumption of digital logic and memory circuits.

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The band-to-band tunneling of monolayer transition metal dichalcogenides nano-junction is investigated using atomistic ab initio quantum transport simulations. From the simulation, it is found that the transition metal vacancy defect in the two-dimensional MX2 (M = Mo,W; X = S,Se) band-to-band tunneling diode can dramatically boost the on-state current up to 10 times while maintaining the device sub-threshold swing. The performance enhancement mechanism is discussed in detail by examining partial density of states of the system. It is found that the transition metal vacancy induces band-gap states, which reduce the effective length of the tunneling transition region.

Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall and energy efficiency bottleneck in the von Neumann system and to mitigate the stagnation of Moore’s law. However, an ideal artificial neuron possessing bio-inspired behaviors as exemplified by the requisite leaky-integrate-fire and self-reset (LIFT) functionalities within a single device is still lacking. Here, we report a new type of spiking neuron with LIFT characteristics by manipulating the magnetic domain wall motion in a synthetic antiferromagnetic (SAF) heterostructure. We validate the mechanism of Joule heating modulated competition between the Ruderman–Kittel–Kasuya–Yosida interaction and the built-in field in the SAF device, enabling it with a firing rate up to 17 MHz and energy consumption of 486 fJ/spike. A spiking neuron circuit is implemented with a latency of 170 ps and power consumption of 90.99 μW. Moreover, the winner-takes-all is executed with a current ratio >104 between activated and inhibited neurons. We further establish a two-layer spiking neural network based on the developed spintronic LIFT neurons. The architecture achieves 88.5% accuracy on the handwritten digit database benchmark. Our studies corroborate the circuit compatibility of the spintronic neurons and their great potential in the field of intelligent devices and neuromorphic computing.

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