论文标题
用于基于自旋的神经形态计算的超低功率域墙设备
Ultra-low Power Domain Wall Device for Spin-based Neuromorphic Computing
论文作者
论文摘要
神经形态计算（NC）正在作为实现低功率智能设备的潜在技术广泛接受。为了实现NC，研究人员研究了各种类型的合成神经元和突触设备，例如新闻和自旋域壁（DW）设备。相比之下，基于DW的神经元和突触具有更高的耐力。但是，要实现低功率设备，需要低能的DW运动（通常低于PJ/位）。在这里，我们通过调整β-W旋转厅材料来证明当前密度低至1E6 A/M2的域壁运动。通过我们的设计，我们将超低固定场和电流密度降低10000倍。将域壁移动约20微米的距离所需的能量为0.4 FJ，这转化为20 nm的0.4 AJ/位的能量消耗。使用蜿蜒的域壁器件配置，我们已经建立了一个受控的DW运动，以实现突触应用，并显示了制造基于超低能量旋转的神经形态元素的方向。
Neuromorphic computing (NC) is gaining wide acceptance as a potential technology to achieve low-power intelligent devices. To realize NC, researchers investigate various types of synthetic neurons and synaptic devices such as memristors and spintronic domain wall (DW) devices. In comparison, DW-based neurons and synapses have potentially higher endurance. However, for realizing low-power devices, DW motion at low energies - typically below pJ/bit - are needed. Here, we demonstrate domain wall motion at current densities as low as 1E6 A/m2 by tailoring the beta-W spin Hall material. With our design, we achieve ultra-low pinning fields and current density reduction by a factor of 10000. The energy required to move the domain wall by a distance of about 20 micrometers is 0.4 fJ, which translates into energy consumption of 0.4 aJ/bit for a bit-length of 20 nm. With a meander domain wall device configuration, we have established a controlled DW motion for synapse applications and have shown the direction to make ultra-low energy spin-based neuromorphic elements.
