In this article, the focus is on using machine learning methods to analyse non-volatile memory devices. This is important because the production of integrated circuits in the sub-micrometre range depends on advancements in manufacturing process technology, and it is crucial to evaluate how manufacturing tolerances affect the functionality of contemporary integrated circuits. Traditionally, Monte Carlo-based techniques have been used to accurately evaluate the impact of manufacturing tolerances on the functionality of integrated circuits. However, these techniques are computationally time-consuming. We will propose a scheme to "learn" the variation of the read margin (parallel and anti-parallel resistance) performance of spintronics devices. The machine learning approach, artificial neural network, is focused on this study (Read margin of spin transfer torque (STT)) spintronics devices. The accuracy for STT by Artificial Neural Network (ANN) is calculated with the help of the MATLAB deep learning toolbox. Regression models using machine learning (ML) are fast and precise over a variety of input ranges, making them ideal for device modelling. The ML algorithm has emerged as a potential substitute for Monte Carlo-based techniques. It can reduce the computational load needed in a Monte Carlo simulation covering all process corners, design parameters, and operating conditions. The article demonstrates the effectiveness of the ML algorithm by performing various simulations on spin transfer torque (STT) non-volatile memory. The proposed scheme involves "learning" the variation of the read margin performance of spintronic devices as a function of its material and geometric parameters. In conclusion, the use of machine learning techniques based on the different regression methods is a promising approach to increase the prediction time of result analysis as compared to SPICE simulation time.
The propagation of spin waves and their interaction with the spin solitons like skyrmions, domain walls and vortex are one of the promising ways for designing nanoscale spintronic devices. Magnetic skyrmion, a particle-like nanoscale object has potential applications in next-generation spintronic devices. In this paper, the unidirectional motion of the skyrmion under the influence of spin wave is studied using micromagnetic simulations. Here, two different magnetic anisotropies are considered on a nanotrack that creates an energy gradient. As a result, the repulsive forces act on the skyrmion and is responsible for the motion of the skyrmion in one direction. The spin wave driving force leads the skyrmion to move in forward direction and the anisotropy gradient is responsible to prevent the skyrmion motion in reverse direction. The skyrmion moves from higher perpendicular magnetic anisotropy region to lower energy region, leading to a unidirectional transport of the skyrmion. This proposed device has less Joule heating and is more energy efficient as compared to other spin transfer torque (STT) and spin orbit torque (SOT) driven techniques. This is due to the fact that spin wave can generate a flow of magnetic momentum without generating an electron flow. This spin wave driven skyrmionics device is a promising pathway towards the development of a complete non-charge based magnetic devices.
Spintronics has attracted considerable interest for next-generation nano-devices because of their low power consumption, unlimited endurance, and non-volatility. Although spin-transfer-torque and spin-orbit-torque are widely used magnetization switching mechanisms, they are still limited by high power consumption and low switching speed. On the other hand, optically assisted magnetization switching using ultrashort laser pulses is able to achieve sub-picosecond switching operation. However, ferromagnetic materials require multiple laser pulses to switch their magnetization, that leads to higher energy consumption as compared to ferrimagnetic materials. In this paper, optically assisted magnetization dynamics in Ho-Fe-Co ferromagnetic nanostructure has been investigated using atomistic spin and monte carlo simulations. Ho has a relatively high magnetic moment and enhances magnetic anisotropy in Ho-Fe-Co nanostructure to achieve single shot and energy-efficient magnetization switching at room temperature.
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