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R&D: Granular Magnetization Switching in Pt/Co/Ti Structure with HfOx Insertion for In-Memory Computing Applications

Results identify that DW nucleation with Pt/Co/HfOx/Ti based device has potential applications in multistate storage and ANN.

Nano Letters has published an article written by Tianli Jin, Bo Zhang, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore, and Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education, Lanzhou University, Lanzhou, 730000, China, Funan Tan, Gerard Joseph Lim, Ze Chen, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore, Jiangwei Cao, Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education, Lanzhou University, Lanzhou, 730000, China, and Wen Siang Lew, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.

Abstract: Exploring multiple states based on the domain wall (DW) position has garnered increased attention for in-memory computing applications, particularly focusing on the utilization of spin–orbit torque (SOT) to drive DW motion. However, devices relying on the DW position require efficient DW pinning. Here, we achieve granular magnetization switching by incorporating an HfOx insertion layer between the Co/Ti interface. This corresponds to a transition in the switching model from the DW motion to DW nucleation. Compared to the conventional Pt/Co/Ti structure, incorporation of the HfOx layer results in an enhanced SOT efficiency and a lower switching current density. We also realized stable multistate storage and synaptic plasticity by applying pulse current in the Pt/Co/HfOx/Ti device. The simulation of artificial neural networks (ANN) based on the device can perform digital recognition tasks with an accuracy rate of 91%. These results identify that DW nucleation with a Pt/Co/HfOx/Ti based device has potential applications in multistate storage and ANN.“

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