Tuning a binary ferromagnet into a multi-state synapse with spin-orbit torque induced plasticity
arXiv:1810.09064 · doi:10.1002/adfm.201808104
Abstract
Inspired by ion-dominated synaptic plasticity in human brain, artificial synapses for neuromorphic computing adopt charge-related quantities as their weights. Despite the existing charge derived synaptic emulations, schemes of controlling electron spins in ferromagnetic devices have also attracted considerable interest due to their advantages of low energy consumption, unlimited endurance, and favorable CMOS compatibility. However, a generally applicable method of tuning a binary ferromagnet into a multi-state memory with pure spin-dominated synaptic plasticity in the absence of an external magnetic field is still missing. Here, we show how synaptic plasticity of a perpendicular ferromagnetic FM1 layer can be obtained when it is interlayer-exchange-coupled by another in-plane ferromagnetic FM2 layer, where a magnetic-field-free current-driven multi-state magnetization switching of FM1 in the Pt/FM1/Ta/FM2 structure is induced by spin-orbit torque. We use current pulses to set the perpendicular magnetization state which acts as the synapse weight, and demonstrate spintronic implementation of the excitatory/inhibitory postsynaptic potentials and spike timing-dependent plasticity. This functionality is made possible by the action of the in-plane interlayer exchange coupling field which leads to broadened, multi-state magnetic reversal characteristics. Numerical simulations, combined with investigations of a reference sample with a single perpendicular magnetized Pt/FM1/Ta structure, reveal that the broadening is due to the in-plane field component tuning the efficiency of the spin-orbit-torque to drive domain walls across a landscape of varying pinning potentials. The conventionally binary FM1 inside our Pt/FM1/Ta/FM2 structure with inherent in-plane coupling field is therefore tuned into a multi-state perpendicular ferromagnet and represents a synaptic emulator for neuromorphic computing.
37 pages with 11 figures, including 20 pages for manuscript and 17 pages for supplementary information
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