Low Power HfOx/TaOx stacked memristors with nanocolumn electrode for neuromorphic computing

Emulating biological synaptic behavior using the resistive random access memory (RRAM) is promising for neuromorphic applications. A stacked HfOx/TaOx RRAM model with nanocolumn electrode for low-power neuromorphic computing was constructed, and the finite element method was used to simulate the reset and set processes. As the local electric field was enhanced by the nanocolumn electrode structure, the superior conductive filament control and lower reset/set voltages can be achieved. Meanwhile, the distributions of oxygen vacancy concentration and temperature during switching processes indicate that the nanocolumn electrode significantly reduces the number of programming pulses required for conductance modulation and lower the power consumption of the array. Meantime, the device also exhibits better conductance linearity (long-term potentiation/long-term depression), which is beneficial for improving the accuracy of neural networks. Then the system-level validation was conducted by integrating the device characteristics into a crossbar array and training with the MNIST dataset using backpropagation, achieving 89.83% recognition accuracy, which is superior to that of plane electrode devices (88.67%). This work demonstrates the potential of nanocolumn electrode induced memristors in realizing efficient neuromorphic computing systems, from device physics to system simulation.