Enhanced Resistive Switching Performances of Mn-Doped BiFeO$_{\text{3}}$ Memristor by Introducing Oxygen Reservoir Interface

In this work, Mn-doped BiFeO $_{\text{3}}$ (BFMO) is employed as the functional layer of the memristor, and the resistive properties of the BFMO-based conductive filament-type memristors are modulated by altering the bottom electrode material (SrRuO $_{\text{3}}$ and TiN) of the device and designing the oxygen-deficient and oxygen-reserving interfaces. Compared to Au/BFMO/SRO memristors with the oxygen-deficient interface, the cycling stability, ON/OFF ratio, retention properties, endurance performances, and multivalue characteristics of Au/BFMO/TiN memristors are improved significantly by introducing oxygen-reserving interfaces. In addition, BFMO memristors with oxygen-reserving interface show lower nonlinearity factors of long-term potentiation (LTP) and long-term depression (LTD) and demonstrate higher recognition accuracy of 98.82% in convolutional neural network (CNN) for the Mixed National Institute of Standards and Technology (MNIST) handwriting recognition. Furthermore, considering different line resistances between the two BFMO-based memristors, a 128 $\times$ 128 array was involved to investigate the influences of the line resistance problem on IR-drop and network recognition accuracy.