MoO<sub><i>x</i></sub> Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware
Xiaofei Dong, Hao Sun, Xinhua Lai, Fengxia Yang, Tingting Ma, Xiang Zhang, Jianbiao Chen, Yun Zhao, Jiangtao Chen, Xuqiang Zhang, Yan Li
Abstract
Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoO x memristors that exhibit nonvolatile ultrafast switching (0.6–0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>10 4 ), a long durability (>10 4 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.