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.Comment: main text 18 pages, supplementary information 10 page