Xiang Xueqiang, Xu Jiankang, Zhang Zhongfang, Jiang Siyuan, Wang Yalong, Wu Biao, Wang Wei, Hou Xiaohu, Xu Guangwei, Zhao Xiaolong, Gao Nan, Long Shibing
School of Microelectronics, University of Science and Technology of China, Hefei 230026, China.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China.
Nano Lett. 2024 Sep 11;24(36):11187-11193. doi: 10.1021/acs.nanolett.4c02340. Epub 2024 Aug 14.
Antiferromagnets (AFMs) are ideal materials to boost neuromorphic computing toward the ultrahigh speed and ultracompact integration regime. However, developing a suitable AFM neuromorphic memory remains an aspirational but challenging goal. In this work, we construct such a memory based on the CoO/Pt heterostructure, in which the collinear insulating AFM CoO shows a strong perpendicular anisotropy facilitating its electrical readout and writing. Utilizing the unique nonlinear response and bipolar fading memory properties of the device, we demonstrate a multidimensional reservoir computing beyond the traditional binary paradigm. These results are expected to pave the way toward next-generation fast and massive neuromorphic computing.
反铁磁体（AFM）是推动神经形态计算向超高速和超紧凑集成方向发展的理想材料。然而，开发一种合适的反铁磁体神经形态存储器仍然是一个令人向往但具有挑战性的目标。在这项工作中，我们基于CoO/Pt异质结构构建了这样一种存储器，其中共线绝缘反铁磁体CoO表现出很强的垂直各向异性，有利于其电读出和写入。利用该器件独特的非线性响应和双极衰退记忆特性，我们展示了一种超越传统二进制范式的多维储层计算。这些结果有望为下一代快速、大规模神经形态计算铺平道路。
