基于自旋分裂扭矩的自重置人工神经元：通过合成反铁磁耦合
Spin splitting torque enabled artificial neuron with self-reset via synthetic antiferromagnetic coupling
AI总结：
本研究提出一种基于自旋分裂扭矩和合成反铁磁耦合的自旋电子学神经元，通过垂直极化自旋实现自重置功能，并在MNIST和N-MNIST数据集上实现了高准确率的SNN应用。
摘要：
自旋电子学人工神经元是高效神经形态计算（NC）的重要构建块。然而，大多数当前实现依赖于对称破坏的外部平面磁场（H_X）来操作神经元，这限制了可扩展性和硬件实用性。我们实验性地展示了一种基于交替磁体/合成反铁磁耦合（SAF）的自旋电子学神经元，该神经元利用垂直方向的自旋（σ_Z）极化自旋分裂扭矩来消除对外部H_X的必要性。该神经元设备还具有由内置交换耦合促进的内在自重置功能。此外，所提出的设备通过在MNIST和N-MNIST数据集上分别实现95.99%和94.36%的测试准确率，验证了其在脉冲神经网络（SNN）应用中的可行性。这些结果展示了所提出自旋电子学神经元的硬件可行性和兼容性，突显了其在紧凑、可扩展和节能神经形态计算系统中的潜力。
英文摘要：
Spintronic artificial neurons are intriguing building blocks for energy efficient Neuromorphic Computing (NC). Nevertheless, most contemporary implementations rely on symmetry breaking external in plane magnetic fields (H_X) for neuron operation, which limits scalability and hardware practicality. We experimentally demonstrate an altermagnet/Synthetic Antiferromagnetic Coupling (SAF) based spintronic neuron that uses out of plane spin (σ_Z) polarized spin-splitting torque to eliminate the necessity of an external H_X. The neuron device also features intrinsic self-reset function facilitated by built-in exchange coupling. Furthermore, the proposed device is validated for Spiking Neural Network (SNN) applications by achieving test accuracies of 95.99% and 94.36% on the MNIST and N-MNIST datasets, respectively. These results demonstrate the hardware feasibility and compatibility of the proposed spintronic neuron, highlighting its potential for compact, scalable and energy-efficient neuromorphic computing systems.
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