Magnetic skyrmion-based data storage and unconventional computing devices have gained increasing attention due to their topological protection, small size, and low driving current. However, skyrmion creation, deletion, and motion are still being studied. In this study, we propose a skyrmion-based neuromorphic magnetic tunnel junction (MTJ) device with both long-and short-term plasticity (LTP and STP) (mixed synaptic plasticity). We showed that plasticity could be controlled by the magnetic field, spinorbit torque (SOT), and the voltage-controlled magnetic anisotropy (VCMA) switching mechanism. LTP depends on the skyrmion density and is manipulated by the SOT and magnetic field while STP is controlled by the VCMA. The LTP property of the device was utilized for static image recognition. By incorporating the STP feature, the device gained additional temporal filtering ability and could adapt to a dynamic environment. The skyrmions were conserved and confined to a nano track to minimize the skyrmion nucleation energy. The synapse device was trained and tested for emulating a deep neural network. We observed that when the skyrmion density was increased, the inference accuracy improved: 90% accuracy was achieved by the system at the highest density. We further demonstrated the dynamic environment learning and inference capabilities of the proposed device.
We present a magnetic tunnel junction (MTJ) based implementation of the spike time-dependent (STDP) learning for pattern recognition applications. The proposed hybrid scheme utilizes the spin-orbit torque (SOT) driven neuromorphic device-circuit co-design to demonstrate the Hebbian learning algorithm. The circuit implementation involves the (MTJ) device structure, with the domain wall motion in the free layer, acting as an artificial synapse. The post-spiking neuron behaviour is implemented using a low barrier MTJ. In both synapse and neuron, the switching is driven by the spin-orbit torques generated by the spin Hall effect in the heavy metal. A coupled model for the spin transport and switching characteristics in both devices is developed by adopting a modular approach to spintronics. The thermal effects in the synapse and neuron result in a stochastic but tuneable domain wall motion in the synapse and a superparamagnetic behaviour of in neuron MTJ. Using the device model, we study the dimensional parameter dependence of the switching delay and current to optimize the device dimensions. The optimized parameters corresponding to synapse and neuron are considered for the implementation of the Hebbian learning algorithm. Furthermore, cross-point architecture and spike time-dependent plasticity-based weight modulation scheme is used to demonstrate the pattern recognition capabilities by the proposed neuromorphic circuit.
Tunnel magneto-resistance (TMR), thermal stability, and critical switching current are important metrics of a magnetic tunnel junction (MTJ). In this work, a detailed study of these metrics is conducted for the down-scaling of the transverse dimensions of the MTJ. The quantum transport and the magnetization dynamics simulations are performed using nonequilibrium Green's function in the mode-space approach and Object Oriented Micromagnetic Framework (OOMMF), respectively. The study of areal size quantization effects on the TMR shows that most of the contribution to the TMR comes from lower energy sub-bands and that the TMR saturates for dimensions above 50 nm. An anomalous behavior is observed in the bias dependence of TMR for the lower energy sub-bands and is explained in terms of the modified Slonczewski's analytical model for conductance around zero bias. The study of TMR scaling is extended to consider non-idealities by introducing elastic dephasing into our simulations. It is shown that with down-scaling of diameters, dephasing affects the zero bias TMR predominantly below 20 nm. Further, TMR is also studied in terms of sensitivity to the variations in the interface layer and the asymmetric reduction of TMR with bias and its reversal at higher bias is observed. OOMMF simulations of the larger stack, including the free layer, are carried out to understand the qualitative link between magnet switching behavior, thermal stability, and critical current density with area scaling. It is shown that the area dependence of thermal stability and critical current follow each other qualitatively and the scaling of both these metrics is correlated to different regimes of magnetization switching such as macrospin behavior or formation of metastable complex textures. The implications of scaling, on the various MTJ metrics, are discussed in terms of the application domain.
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