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

• Published in: Advanced functional materials Vol. 34; no. 9
• Main Authors: Weng, Zhengjin, Zheng, Haofei, Lei, Wei, Jiang, Helong, Ang, Kah‐Wee, Zhao, Zhiwei
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.02.2024
• Subjects: Artificial neural networks; convolutional image processing; Crystal defects; Electric potential; Handwriting recognition; Image processing; Manganese; manganese phosphorus trisulfides; Memristors; Neuromorphic computing; Phosphorus; Supervised learning; Switching; Titanium; Voltage
• ISSN: 1616-301X; 1616-3028
• DOI: 10.1002/adfm.202305386

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 (104 s), fast switching speed (≈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. Here, high‐yield, high‐performance, and uniform Ti/MnPS3/Au memristors are demonstrated to possess the desired characteristics for neuromorphic computing. Microscopic investigation on structural and chemical characteristics of the few‐layer MnPS3 reveals that introduced structural defects and residual Ti can guide the formation of conductive filaments at a low set voltage with minimal variability.