Spintronic Device Potential in Neuromorphic Computing from a Device-Circuit Modeling Perspective

Recent years have seen tremendous progress in the area of neuromorphic computing, which seeks to mimic the parallel processing power and energy efficiency of the human brain. From the standpoint of device-circuit modelling, this work explores how spintronic devices might improve the scalability and performance of neuromorphic systems. Utilizing the inherent spin of the electron and the magnetic moment that goes along with it, spintronics has special benefits in terms of non-volatility, low power consumption, and high integration density. We provide a thorough model that accurately depicts the complex behaviour of spintronic components and how they are incorporated into neuromorphic circuits. Our results suggest that spintronic devices may improve the overall efficiency of neuromorphic systems by reducing the power-delay product substantially. Furthermore, it is possible to realize synaptic capabilities and plasticity processes with better accuracy by using the intrinsic features of spintronic devices, such as magnetoresistance and spin-transfer torque. Additionally, the research identifies problems with device variability and suggests possible fixes. In summary, spintronic devices have great potential to transform the field of neuromorphic computing if they are properly modelled and integrated.