Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy‐Efficient Neuromorphic Hardware

Designing reliable and energy‐efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3) are reported that exhibit several favorable characteristics...

Full description

Saved in:
Bibliographic Details
Published in:Small (Weinheim an der Bergstrasse, Germany) Germany), 2024-02, Vol.20 (5), p.e2304518-n/a
Main Authors: Weng, Zhengjin, Zheng, Haofei, Li, Lingqi, Lei, Wei, Jiang, Helong, Ang, Kah‐Wee, Zhao, Zhiwei
Format: Article
Language:English
Subjects:
Arrays
Artificial neural networks
Chemical properties
Filaments
Memristors
multipattern memorizations
multiply‐and‐accumulate operations
Neuromorphic computing
Nickel
nickel phosphorus trisulfides
Pattern recognition
Phosphorus
Switching
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Designing reliable and energy‐efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (102), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X‐ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS3/Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS3 suggests that the penetration of Ti ions is accompanied by the drift of phosphorus‐sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi‐reset mode, effectively emulates long‐term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply‐and‐accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems. Reliable and energy‐efficient synaptic crossbar arrays based on layered NiPS3 are reported. Concrete experimental evidence reveals the complex structure and phase evolution of NiPS3 upon the electrochemical metallization process. Exploiting the highly linear and symmetric weight update characteristics, image recognition with a high accuracy of 96.4% is achieved in artificial neural network simulation.
ISSN:1613-6810
1613-6829
DOI:10.1002/smll.202304518