Che Chengfang, Hou Juan, Yang Chendi
College of Sciences, Shihezi University, Shihezi 832003, China.
Academy for Engineering & Technology, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai 200438, China.
ACS Appl Mater Interfaces. 2025 Jun 18;17(24):35912-35920. doi: 10.1021/acsami.5c04834. Epub 2025 Jun 5.
The integration of skyrmions in 2D ferromagnetic materials and advanced spintronic devices enables energy-efficient computing for artificial synapses in neuromorphic architecture. Nucleation of skyrmions plays a critical role in ensuring high reliability and low energy consumption. However, the key challenge lies in selectively generating skyrmions, excluded from trivial bubbles and their controllable transition. Here, as a concept example of 2D ferromagnets, we demonstrate the evolution of the skyrmion in FeGeTe excluded from the trivial bubbles within the geometrically confined 1 μm nanodisk. The skyrmions selectively nucleate from stripe domains, driven by the competition between dipolar-dipolar interactions and Zeeman energy, captured in real-time/space using in situ Lorentz TEM. Furthermore, we reveal a reversible skyrmion-to-trivial bubble transition in the 500 nm nanodisk induced by an in-plane magnetic field, confirmed by the micromagnetic simulation. The reversible switching of skyrmions and trivial bubbles enables the implementation of a binary-state neuromorphic computing framework. The skyrmion-based artificial synapses demonstrate over 85% handwriting recognition accuracy. The findings bridging fundamental skyrmion physics with practical applications offer key insights for designing next-generation 2D materials-based devices.
将斯格明子集成到二维铁磁材料和先进的自旋电子器件中，可为神经形态架构中的人工突触实现节能计算。斯格明子的成核在确保高可靠性和低能耗方面起着关键作用。然而，关键挑战在于选择性地生成斯格明子，避免产生平凡气泡以及实现其可控转变。在此，作为二维铁磁体的一个概念示例，我们展示了在几何受限的1μm纳米盘中，FeGeTe中斯格明子从平凡气泡中分离出来的演化过程。斯格明子由偶极 - 偶极相互作用和塞曼能之间的竞争驱动，从条纹域中选择性地成核，利用原位洛伦兹透射电子显微镜实时/空间捕获。此外，我们揭示了由面内磁场诱导的500nm纳米盘中斯格明子到平凡气泡的可逆转变，这通过微磁模拟得到证实。斯格明子和平凡气泡的可逆切换实现了二元态神经形态计算框架。基于斯格明子的人工突触展示了超过85%的手写识别准确率。这些将基础斯格明子物理与实际应用相联系的发现，为设计下一代基于二维材料的器件提供了关键见解。
