Demonstration of Synaptic Behavior in a Heavy-Metal-Ferromagnetic-Metal-Oxide-Heterostructure-Based Spintronic Device for On-Chip Learning in Crossbar-Array-Based Neural Networks Demonstration of Synaptic Behavior in a Heavy-Metal-Ferromagnetic-Metal-Oxide-Heterostructure-Based Spintronic Device for On-Chip Learning in Crossbar-Array-Based Neural Networks AITranslate

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DOI：https://doi.org/10.1021/acsaelm.2c01488

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Nanomagnetic and spintronic devices, which make use of physical phenomena in materials and interfaces like perpendicular magnetic anisotropy (PMA) and spin–orbit torque (SOT) to exhibit multiple electrically readable and controllable states, have been widely considered as synaptic elements in analog crossbar arrays for on-chip learning of analog neural networks (ANN). Here, in such a heavy-metal-ferromagnetic-metal-oxide-heterostructure-based (Pt/Co/SiO2) spintronic device, multiple mixed states are first demonstrated experimentally. These mixed states correspond to different magnetization configurations between two saturated states: all magnetic moments vertically up and all moments vertically down (the ferromagnetic layer exhibits PMA). These mixed states are then modulated through in-plane-current pulses, which result in SOT at the heavy-metal–ferromagnet interface, and thus long-term potentiation (LTP) and long-term depression (LTD) are demonstrated in the device (synaptic behavior). The experimentally obtained LTP-and-LTD behavior is explained qualitatively through micromagnetic simulations, which model the interface phenomena phenomenologically. The synaptic bit resolution is then determined experimentally to be 5 (≈ 30 distinguishable states) by measuring the stability of the mixed states. The nonlinearity and asymmetry in the obtained LTP and LTD are quantified experimentally. Next, a crossbar-array-based ANN is simulated using spintronic-synapse-device models based on the experimentally obtained LTP and LTD, and reasonably high classification accuracy is predicted for MNIST and Fashion-MNIST data sets, despite the synaptic nonidealities like nonlinearity, asymmetry, and cycle-to-cycle variations. The impact of nonlinearity and asymmetry on classification accuracy is found to be much higher than that due to limited bit resolution (we do not go below 10 bits per synapse cell though) and cycle-to-cycle variations.