Au‐Nanodots Embedded Self‐Rectifying Analog Charge Trap Memristor with Modified Bias Voltage Application Method for Stable Multi‐Bit Hardware‐Based Neural Network

Abstract

The self‐rectifying memristor with a bilayer of trap‐rich HfO2 and insulating Ta2O5 oxide layers is considered one of the most promising candidates for the memristive crossbar array due to its superior switching performance, scalability with 3D stacking, and low operating power. However, the output current variation due to the electron detrapping from trap states can cause the failure of critical operations in neuromorphic applications. This work suggests two solutions to mitigate the switching variations and insufficient data retention time by embedding gold nanodots and modifying the bias voltage application methods for read, write, and erase operations in the crossbar array. The switching mechanism is studied by varying the embedded position of the gold nanodots across the thickness direction of the bilayered oxides, which helped to optimize device performance further. Combining the two solutions into the proposed self‐rectifying memristor enables a single device to have the 7‐possible, stable states by preventing interstate overlap and securing the retention. Consequently, the hardware neural network consisting of self‐rectifying memristors with gold nanodots with the modified bias voltage application methods demonstrates a high inference accuracy of 93.1% in MNIST handwritten digit classification, comparable to the software‐based accuracy of 93.4%, benefiting from the enhanced multi‐state uniformity.