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...

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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
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cited_by cdi_FETCH-LOGICAL-c4138-43dfad60f37e3d1e7d47fdc8e685350fb9bf1543df3ce889b7d827bfc89de0bb3
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container_title Small (Weinheim an der Bergstrasse, Germany)
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creator Weng, Zhengjin
Zheng, Haofei
Li, Lingqi
Lei, Wei
Jiang, Helong
Ang, Kah‐Wee
Zhao, Zhiwei
description 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.
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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 (&lt;1 V), fast switching speed (&lt; 20 ns), high On/Off ratio (&gt;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. 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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. 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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
title Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy‐Efficient Neuromorphic Hardware
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