Tuning a binary ferromagnet into a multi-state synapse with spin-orbit torque induced plasticity

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.Comment: 37 pages with 11 figures, including 20 pages for manuscript and 17 pages for supplementary informatio

Stochastic spin-orbit-torque device as the STDP synapse for spiking neural networks

Neuromorphic hardware as a non-Von Neumann architecture has better energy efficiency and parallelism than the conventional computer. Here, with numerical modeling spin-orbit torque (SOT) device using current-induced SOT and Joule heating effects, we acquire its magnetization switching probability as a function of the input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working. We further demonstrate that the artificial spiking neural network (SNN) built by this SOT device can perform unsupervised handwritten digit recognition with the accuracy of 80% and logic operation learning. Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevicesComment: 8 pages, 5 figure

Efficient Neuromorphic Computing Enabled by Spin-Transfer Torque: Devices, Circuits and Systems

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this thesis demonstrates the encoding of biological neural and synaptic functionalities in the underlying physics of electron spin. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing neuro-mimetic device structures is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations

Tuning a binary ferromagnet into a multi-state synapse with spin-orbit-torque-induced plasticity

Ferromagnets with binary states are limited for applications as artificial synapses for neuromorphic computing. Here, it is shown how synaptic plasticity of a perpendicular ferromagnetic layer (FM1) can be obtained when it is interlayer exchange‐coupled by another in‐plane ferromagnetic layer (FM2), where a magnetic field‐free current‐driven multistate magnetization switching of FM1 in the Pt/FM1/Ta/FM2 structure is induced by spin–orbit torque. Current pulses are used to set the perpendicular magnetization state, which acts as the synapse weight, and spintronic implementation of the excitatory/inhibitory postsynaptic potentials and spike timing‐dependent plasticity are demonstrated. This functionality is made possible by the action of the in‐plane interlayer exchange coupling field which leads to broadened, multistate 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 the Pt/FM1/Ta/FM2 structure with an inherent in‐plane coupling field is therefore tuned into a multistate perpendicular ferromagnet and represents a synaptic emulator for neuromorphic computing, demonstrating a significant pathway toward a combination of spintronics and synaptic electronics