Synapse and resistance switching behavior of La:HfO2/ZrO2/La:HfO2 memristors

The von Neumann bottleneck in traditional computers has hindered the rapid development of artificial intelligence. To improve computational efficiency, memristors have become a preferred device to mimic synaptic behavior and achieve neuromorphic computing, thus attracting widespread attention. In this work, La:HfO2/ZrO2/La:HfO2 thin films were prepared via sol-gel deposition. When Zr was inserted as an interlayer into 6% La-doped HfO2, the significant resistance switching (RS) behavior was detected through voltage scanning over 100 consecutive cycles, and its electrical performance was enhanced compared to the case when there was no interlayer. The presence of an Analog resistance switch enabled the device to effectively simulate synaptic properties such as the long-term potentiation/inhibition, short-term potentiation/inhibition, paired-pulse facilitation, and spike-timing-dependent plasticity learning rules. Moreover, the device exhibited good linearity in weight updates and excellent conductance modulation performance. Utilizing a convolutional neural network architecture, information in a 28x28 pixel array was classified and processed, thereby improving the recognition accuracy of Mixed National Institute of Standards & Technology (MNIST) dataset to 97.5% and that of Fashion-MNIST dataset to 87.0%. These advancements have provided a viable solution for the successful construction of artificial neural network systems in the future.