摘要
Antiferromagnets (AFMs) exhibit spin arrangements with no net magnetization, positioning them as promising candidates for spintronics applications. While electrical manipulation of the single-crystal AFMs, composed of periodic spin configurations, is achieved recently, it remains a daunting challenge to characterize and to manipulate polycrystalline AFMs. Utilizing statistical analysis in data science, we demonstrate that polycrystalline AFMs can be described using a real, symmetric, positive semi-definite, rank-two tensor, which we term the Neel tensor. This tensor introduces a unique spin torque, diverging from the conventional field-like and Slonczewski torques in spintronics devices. Remarkably, Neel tensors can be trained to retain a specific orientation, functioning as a form of working memory. This attribute enables zero-field spin-orbit-torque switching in trilayer devices featuring a heavy-metal/ferromagnet/AFM structure and is also consistent with the X-ray magnetic linear dichroism measurements. Our findings uncover hidden statistical patterns in polycrystalline AFMs and establishes the presence of Neel tensor torque, highlighting its potential to drive future spintronics innovations.