Leveraging Dual Resistive Switching in Quasi-2D Perovskite Memristors for Integrated Non-volatile Memory, Synaptic Emulation, and Reservoir Computing
Zhenwang Luo, Weisheng Wang, Junhui Wu, Guohua Ma, Yanna Hou, Cheng Yang, Xu Wang, Fei Zheng, Zhenfu Zhao, Ziqi Zhao, Li Qiang Zhu, Ziyang Hu
Abstract
The increasing computational demands of artificial intelligence (AI) algorithms are exceeding the capabilities of conventional computing architectures, creating a strong need for novel materials and paradigms. Memristors that integrate diverse resistive switching (RS) behaviors provide a promising avenue for developing novel computing architectures. In this study, we achieve the coexistence of volatile and nonvolatile RS behaviors in quasi-2D perovskite memristor (Q-2DPM). The Q-2DPM exhibits competitive performance as a nonvolatile memory. Multiple synaptic functions have been successfully simulated on Q-2DPM, such as excitatory postsynaptic currents, paired-pulse facilitation, and long-term potentiation/depression. Furthermore, artificial neural networks using Q-2DPM synapses achieve high accuracy in MNIST image classification tasks. The Q-2DPM's inherent characteristics suitable for reservoir computing are also demonstrated through its application in a pulse-stream-based digital classification experiment, showcasing its impressive performance. The elucidation of the dual RS mechanisms within Q-2DPM provides fresh insights into memristor RS behavior and underscores the potential of achieving diverse computational units through a single device. This work paves the way for the implementation of physical neuromorphic hardware architectures and the advancement of sophisticated computational primitives, offering a significant step toward the next generation of computing technologies.