论文标题
基于自旋的神经元和突触设备的设计,用于尖峰神经网络电路
Design of Spintronics-based Neuronal and Synaptic Devices for Spiking Neural Network Circuits
论文作者
论文摘要
拓扑稳定的磁性天际含量较低,含电流密度要低得多,这可能对记忆和神经形态计算有用。但是,基于天际的设备遭受了来自Skyrmion Hall效应的Magnus力,如果驾驶电流的幅度太大,这可能会导致不必要的Skyrmion歼灭。这项工作证明了使用合成的抗磁性耦合双层装置的人造神经元和突触的设计,该双层装置无效,该装置无效。人工泄漏的集成和开火神经元中的泄漏术语是通过工程化神经元设备的单轴各向异性轮廓来实现的。突触设备的结构与神经元设备相似,但具有恒定的单轴各向异性。突触设备还具有线性和对称重量更新,这是人工突触的高度理想特征。还研究了基于磁性域壁(DW)运动的神经元和突触设备,并将其与天空设备进行比较。我们的仿真结果表明,在DW或基于Skyrmion的设备中执行此类操作所需的能量为几个FJ的顺序。
Topologically stable magnetic skyrmion has a much lower depinning current density that may be useful for memory as well as neuromorphic computing. However, skyrmion-based devices suffer from the Magnus force originating from the skyrmion Hall effect, which may result in unwanted skyrmion annihilation if the magnitude of the driving current gets too large. A design of an artificial neuron and a synapse using a synthetic antiferromagnetically coupled bilayer device, which nullifies the Magnus force, is demonstrated in this work. The leak term in the artificial leaky integrate-and-fire neuron is achieved by engineering the uniaxial anisotropy profile of the neuronal device. The synaptic device has a similar structure as the neuronal device but has a constant uniaxial anisotropy. The synaptic device also has a linear and symmetric weight update, which is a highly desirable trait of an artificial synapse. Neuronal and synaptic devices based on magnetic domain-wall (DW) motion are also studied and compared to skyrmionic devices. Our simulation results show the energy required to perform such operation in DW or skyrmion-based devices is on the order of a few fJ.