Dipole coupled nanomagnets controlled by the static Zeeman field can form various magnetic logic interconnects. However, the corner wire interconnect is often unreliable and error-prone at room temperature. In this study, we address this problem by making it into a reliable type with trapezoid-shaped nanomagnets, the shape anisotropy of which helps to offer the robustness. The building method of the proposed corner wire interconnect is discussed, and both its static and dynamic magnetization properties are investigated. Static micromagnetic simulation demonstrates that it can work correctly and reliably. Dynamic response results are reached by imposing an ac microwave field on the proposed corner wire. It is found that strong ferromagnetic resonance absorption appears at a low frequency. With the help of a very small ac field with the peak resonance frequency, the required static Zeeman field to switch the corner wire is significantly decreased by ∼21 mT. This novel interconnect would pave the way for the realization of reliable and low power nanomagnetic logic circuits.
Magnetic quantum-dot cellular automata (MQCA) function arrays are fabricated by electron beam lithography, thermal evaporation and lift-off technologies at room temperature. The effects of exposure dose and exposure time on MQCA function array patterns with three various spacings are experimentally investigated. The results show that the ideal pattern can only be obtained with 100 pA electron beam current and 0.38 μs exposure time. Magnetic force microscopy measurement on the fabricated inverter structure shows that the array demonstrates correct logic operation, which validates the feasibility of fabrication process for MQCA function arrays. Moreover, defect is observed in the experiments, simulations on the defective array show that missing nanomagnet defect in the array leads to signal inversion error.
The spin neuron is an emerging artificial neural device which has many advantages such as ultra-low power consumption, strong nonlinearity, and high integration. Besides, it has ability to remember and calculate at the same time. So it is seen as a suitable and excellent candidate for the new generation of neural network. In this paper, a spin neuron driven by magnetic field and strain is proposed. The micromagnetic model of the device is realized by using the OOMMF micromagnetic simulation software, and the numerical model of the device is also established by using the LLG equation. More importantly, a three-layer neural network is composed of spin neurons constructed respectively using three materials (Terfenol-D, FeGa, Ni). It is used to study the activation functions and the ability to recognize the MNIST handwritten datasets.c Results show that the spin neuron can successfully achieve the random magnetization switching to simulate the activation behavior of the biological neuron. Moreover, the results show that if the ranges of the inputting magnetic fields are different, the three materials' neurons can all reach the saturation accuracy. It is expected to replace the traditional CMOS neuron. And the overall power consumption of intelligent computing can be further reduced by using appropriate materials. If we input the magnetic fields in the same range, the recognition speed of the spin neuron made of Ni is the slowest in the three materials. The results can establish a theoretical foundation for the design and the applications of the new artificial neural networks and the intelligent circuits.
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