Topological whirls or ‘textures’ of spins such as magnetic skyrmions represent the smallest realizable emergent magnetic entities^1,2,3,4,5. They hold considerable promise as robust, nanometre-scale, mobile bits for sustainable computing^6,7,8. A longstanding roadblock to unleashing their potential is the absence of a device enabling deterministic electrical readout of individual spin textures^9,10. Here we present the wafer-scale realization of a nanoscale chiral magnetic tunnel junction (MTJ) hosting a single, ambient skyrmion. Using a suite of electrical and multimodal imaging techniques, we show that the MTJ nucleates skyrmions of fixed polarity, whose large readout signal—20–70% relative to uniformly magnetized states—corresponds directly to skyrmion size. The MTJ exploits complementary nucleation mechanisms to stabilize distinctly sized skyrmions at zero field, thereby realizing three non-volatile electrical states. Crucially, it can electrically write and delete skyrmions to both uniform states with switching energies 1,000 times lower than the state of the art. Here, the applied voltage emulates a magnetic field and, in contrast to conventional MTJs, it reshapes both the energetics and kinetics of the switching transition, enabling deterministic bidirectional switching. Our stack platform enables large readout and efficient switching, and is compatible with lateral manipulation of skyrmionic bits, providing the much-anticipated backbone for all-electrical skyrmionic device architectures^9,10. Its wafer-scale realizability provides a springboard to harness chiral spin textures for multibit memory and unconventional computing^8,11.

拓扑漩涡或自旋“纹理”（例如磁性斯格明子）代表了最小的可实现的涌现磁性实体^1,2,3,4,5。它们作为用于可持续计算的稳健、纳米级移动比特具有广阔的前景^6,7,8。释放其潜力的长期障碍是缺乏能够确定性地电读出各个自旋纹理的设备^9,10。在这里，我们展示了托管单个环境斯格明子的纳米级手性磁隧道结（MTJ）的晶圆级实现。使用一套电学和多模态成像技术，我们表明 MTJ 使固定极性的斯格明子成核，其大读出信号（相对于均匀磁化状态的 20-70%）直接对应于斯格明子的大小。 MTJ 利用互补成核机制在零场下稳定不同尺寸的斯格明子，从而实现三种非易失性电态。至关重要的是，它可以以比现有技术低 1,000 倍的开关能量将斯格明子电写入和删除到两种均匀态。这里，所施加的电压模拟磁场，与传统的 MTJ 相比，它重塑了开关转换的能量和动力学，从而实现确定性双向开关。我们的堆栈平台可实现大读数和高效切换，并且与斯格明子位的横向操作兼容，为全电斯格明子器件架构提供了备受期待的主干^9,10。其晶圆级可实现性为利用手性自旋纹理实现多位内存和非常规计算提供了一个跳板^8,11。