High-Performance Memristors Based on Few-Layer Manganese Phosphorus Trisulfide for Neuromorphic Computing

While transition-metal thiophosphate (MPX3) materials have been a subject of extensive research in recent years, experimental studies on MPX3-based memristors are still in their early stages, with device performance being less than ideal. Here, the successful fabrication of high-yield, high-performance, and uniform memristors are demonstrated to possess desired characteristics for neuromorphic computing using a single-crystalline few-layered manganese phosphorus trisulfide (MnPS3) as a resistive switching medium. The Ti/MnPS3/Au memristor exhibits small switching voltage (<1 V), long memory retention (10(4) s), fast switching speed (approximate to 20 ns), high On/Off ratio (nearly two orders of magnitude), and simultaneously achieves emulation of synaptic weight plasticity. The microscopic investigation of the structural and chemical characteristics of the few-layer MnPS3 reveals the presence of structural defects and residual Ti throughout the stacked layer following the application of voltage, which contributes to the uniformity of switching with a low set voltage. With highly linear and symmetric analog weight updates coupled with the capability of accurate decimal arithmetic operations, a high accuracy of 95.15% in supervised learning using the MNIST handwritten recognition dataset is achieved in the artificial neural network. Furthermore, convolutional image processing can be implemented using hardware multiply-and-accumulate operation in an experimental memristor crossbar array.