LAUSR

In crossbar array structures, which serves as an “in-memory” compute engine for artificial intelligence (AI) hardware, write sneak path problem causes undesired switching of devices that degrades network accuracy. While custom crossbar programming schemes have been proposed, device-level innovations leveraging nonlinear switching characteristics of the cross-point devices are still under exploration to improve the energy eff iciency of the write process. In this work, a spintronic device design based on magnetic tunnel junction (MTJ) exploiting the use of voltage-controlled magnetic anisotropy (VCMA) effect is proposed as a solution to the write sneak path problem. In addition, insights are provided regarding appropriate operating voltage conditions to preserve the robustness of the magnetization trajectory during switching, which is critical for proper switching probability manipulation.