Rahaman Hasibur, Kumar Durgesh, Chung Hong Jing, Maddu Ramu, Lim Sze Ter, Jin Tianli, Piramanayagam S N
School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371.
Institute of Materials Research and Engineering, 2 Fusionopolis Way, Singapore 138634.
ACS Appl Mater Interfaces. 2023 Mar 29;15(12):15832-15838. doi: 10.1021/acsami.2c20905. Epub 2023 Mar 15.
Neuromorphic computing (NC) is considered a potential vehicle for implementing energy-efficient artificial intelligence. To realize NC, several technologies are being investigated. Among them, the spin-orbit torque (SOT)-driven domain wall (DW) devices are one of the potential candidates. Researchers have proposed different device designs to achieve neurons and synapses, the building blocks of NC. However, the experimental realization of DW device-based NC is only at the primeval stage. Here, we have studied pine-tree DW devices, based on the Laplace pressure on the elastic DWs, for achieving synaptic functionalities and diode-like characteristics. We demonstrate an asymmetric pinning strength for DW motion in two opposite directions to show the potential of these devices as DW diodes. We have used micromagnetic simulations to understand the experimental findings and to estimate the Laplace pressure for various design parameters. The study provides a strategy to fabricate a multifunctional DW device, exhibiting synaptic properties and diode characteristics.
神经形态计算（NC）被认为是实现高能效人工智能的一种潜在途径。为了实现神经形态计算，人们正在研究多种技术。其中，自旋轨道扭矩（SOT）驱动的畴壁（DW）器件是潜在的候选方案之一。研究人员已经提出了不同的器件设计来实现神经元和突触，它们是神经形态计算的基本组成部分。然而，基于DW器件的神经形态计算的实验实现仍处于初始阶段。在此，我们基于弹性畴壁上的拉普拉斯压力研究了松树状畴壁器件，以实现突触功能和类二极管特性。我们展示了畴壁在两个相反方向上运动时的不对称钉扎强度，以表明这些器件作为畴壁二极管的潜力。我们使用微磁模拟来理解实验结果，并估计各种设计参数下的拉普拉斯压力。该研究提供了一种制造多功能畴壁器件的策略，该器件具有突触特性和二极管特性。
