Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan.
Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan.
Sci Rep. 2023 Mar 31;13(1):5260. doi: 10.1038/s41598-023-32084-8.
Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir computing. Among various magnetic materials, spin glass is known to exhibit slow magnetic relaxation that has the potential to offer the short-term memory capability. In this research, we have quantitatively investigated the short-term memory capability of spin cluster glass based on the prevalent benchmark. The results reveal that the magnetization relaxation of Co, Si-substituted LuFeO with spin glass behavior can provide higher short-term memory capacity than ferrimagnetic material without substitution. Therefore, materials with spin glass behavior can be considered as potential candidates for constructing next-generation spintronic reservoir computing with better performance.
储层计算是一种大脑启发式计算范例，可以高速完成训练。储层计算系统的学习性能依赖于其非线性和短期记忆能力。作为物理实现，基于自旋的储层计算由于其低功耗和小尺寸而引起了相当大的关注。然而，很少有研究关注开发自旋电子储层计算中材料本身的短期记忆能力。在各种磁性材料中，自旋玻璃以其缓慢的磁弛豫而闻名，这种弛豫有可能提供短期记忆能力。在这项研究中，我们基于流行的基准定量研究了基于自旋团簇玻璃的短期记忆能力。结果表明，具有自旋玻璃行为的 Co、Si 取代的 LuFeO 的磁化弛豫可以比没有取代的亚铁磁材料提供更高的短期记忆能力。因此，具有自旋玻璃行为的材料可以被认为是构建具有更好性能的下一代基于自旋的储层计算的潜在候选材料。
