Design of behavior prediction model of molybdenum disulfide magnetic tunnel junctions using deep networks

Abstract Magnetic tunnel junctions (MTJs) are widely used in spintronics development owing to their high scalability and minimal power consumption. However, analyzing the electrical and magnetic behaviors of MTJs in real-time applications is challenging. In this study, an MTJ based on molybdenum disulfide (MoS2) wasdesigned, and a novel deep Elman neural behavior prediction model wasdeveloped to analyze its behavior. In the proposed model, MoS2 acts as a tunnel barrier, and iron oxide (Fe3O4) acts as a ferromagnetic electrode. The interface between Fe3O4 and MoS2 in the MTJ improves the spin polarization and tunnel magnetoresistance ratio. Herein, the performance parameters of the MTJ wereused as inputs for the developed prediction model, which analyzes the magnetic and electrical properties of the MTJ using prediction parameters. The spin currents in the parallel and anti-parallel configurations werealso determined. The designed model wasimplemented using MATLAB and validated through comparison of simulation and experimental results.