Neuromorphic computing using multilevel nonvolatile memories as synapses offers opportunities for future energy‐ and area‐efficient artificial intelligence. Among these memories, artificial synapses based on current‐induced magnetization switching driven by spin–orbit torques (SOTs) have attracted great attention recently. Herein, the gradient descent algorithm, a primary learning algorithm, implemented on a 2 × 1 SOT synaptic array is reported. Successful pattern classifications are experimentally realized through the tuning of cycle‐to‐cycle variation, linearity range, and linearity deviation of the multilevel SOT synapse. Also, a larger m × n SOT synaptic array with m controlling transistors is proposed and it is found that the classification accuracies can be improved dramatically by decreasing the cycle‐to‐cycle variation. A way for the application of spin–orbit device arrays in neuromorphic computing is paved and the crucial importance of the cycle‐to‐cycle variation for a multilevel SOT synapse is suggested.
We investigated field-free current-induced perpendicular magnetization switching in Pt/Co/AlOx/Co/Ta structures by varying the thickness of an insulating spacer layer. A field-free spin–orbit torque switching is realized through the antiferromagnetic interlayer exchange coupling (IEC) between the bottom and top Co layers or by premagnetizing the top Co layer. Significant variations in magnetic and electrical properties are ascribed to thickness dependent IEC by changing the insulating spacer layer from 1.0 to 1.9 nm. When the thickness of the spacer layer is 1.6 nm, we found the strongest IEC field of about 300 Oe and optimal field-free current-induced magnetization switching. Micromagnetic simulation validates the existence of the Dzyaloshinskii–Moriya interaction (DMI) effect and the chirality of the domain wall configuration in the stack structures, and the field-free deterministic magnetization switching is mainly induced from DMI and IEC fields.
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