Magnetic skyrmion-based brain-inspired devices

<sec><p indent="0mm">Neuromorphic computing mimics the brain’s neural networks, using artificial neurons and synapses to process information. This approach holds promise for more efficient and energy-saving solutions in artificial intelligence (AI) applications. However, traditional semiconductor devices struggle to meet the demands of neuromorphic computing, particularly in terms of non-volatility and integrated memory-processing functions. To advance this field, new hardware solutions are required. Spintronic devices, which utilize the spin of electrons in addition to their charge, offer a promising alternative for neuromorphic computing due to their inherent non-volatility. Among these, magnetic skyrmions—nanoscale, vortex-like magnetic structures—are especially attractive. Their unique topological properties, stability, and low-power manipulation make them ideal for use as information carriers in neuromorphic systems. Skyrmions are stabilized in materials through the Dzyaloshinskii-Moriya interaction (DMI), which breaks spatial inversion symmetry. This stability, combined with the ability to move skyrmions using low-energy electrical currents, makes them highly efficient for information storage and processing. Unlike conventional magnetic domain walls, skyrmions are more stable against external perturbations, providing an advantage in neuromorphic applications, where robustness and energy efficiency are critical. </sec><sec> This review examines recent developments in skyrmion-based neuromorphic devices. It begins by outlining the fundamental physical properties of skyrmions, including their stabilization mechanism and electrical current-driven motion. The review then discusses how these properties can be exploited to replicate the functions of artificial neurons and synapses—the core components of neuromorphic systems. In skyrmion-based artificial neurons, the motion of a skyrmion through a nanowire can simulate the integration of input signals. When the skyrmion reaches a specific point, it triggers the neuron to “fire”, mimicking the behavior of biological neurons. Similarly, skyrmion-based synapses regulate the strength of connections between neurons by adjusting the number of skyrmions in a region, thereby modifying the synaptic weight—a key feature for learning and memory. We then explore different device architectures proposed for implementing these skyrmion-based neurons and synapses. These include structures based on magnetic tunnel junctions and nanostructured spintronic materials, offering benefits in scalability, energy efficiency, and non-volatility. Furthermore, some research shows the potential for these devices to integrate with conventional CMOS technology. Despite significant progress, challenges remain before skyrmion-based neuromorphic devices can be implemented into neuromorphic systems. These include improving skyrmion stability at room temperatures, optimizing material systems for better performance, and achieving integration with existing electronics. Additionally, most current research is either theoretical or in early experimental stages, with large-scale demonstrations yet to be realized. Magnetic skyrmions offer a novel path for neuromorphic computing, providing a potential solution to the limitations of traditional semiconductor devices. With further research, skyrmion-based systems could lead to highly efficient, low-power computing architectures capable of emulating the brain’s complex functions. </sec>