MoO_x Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware

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 (>104), a long durability (>104 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.

Coexistance of the Negative Photoconductance Effect and Analogue Switching Memory in the CuPc Organic Memristor for Neuromorphic Vision Computing

“…The black phosphorus nanosheet–CsPbBr 3 ENOM arrays were proposed to simulate to the lobula giant movement detector for collision prediction . In addition, the memristor with high-performance electrical switching behaviors, such as analogue switching memory, enough conductance states, ultralow voltage operations, etc., − was integrated with light-sensing arrays to simulate the function of the human vision system. − In our previous works, the memristor with light-intensity-tunable positive/negative photoconductance effects or multiconductance levels was developed for the implementation of in-sensing neuromorphic computing. − The above research in neuromorphic visual systems, including ENOT and ENOM, is perusing a multifunctional, high-mimicking, and highly efficient photoelectronic device.…”