Stable Halide Perovskite Memristor Utilizing Innovative Silver/Bismuth Electrode as an Alternative to Gold

Abstract Perovskite memristors hold great promise for neuromorphic computing due to their ease of fabrication and sensitivity to light and electrical stimuli. However, the common use of expensive metal electrodes, such as gold (Au) and unstable silver (Ag), limits their stability and broader application. In this study, a cost‐effective perovskite memristor utilizing a novel Ag/Bismuth (Ag/Bi) bilayer electrode, which serves as a viable alternative to Au while maintaining excellent performance, is presented. This design prevents electrochemical reactions and the formation of unstable metallic filaments, enabling controlled mixed electronic/ionic conductivity. Moreover, the low work function of the Ag/Bi bilayer reduces the built‐in voltage, facilitating the formation and retention of conductive filaments, which improves device performance and stability. The memristor exhibits a high on/off ratio (10 2 ), excellent endurance (≈800 cycles), long retention (>10 4 s), and storage stability comparable to Au‐based devices. Furthermore, it demonstrates neuromorphic synaptic behaviors, including long‐ and short‐term plasticity (STP), potentiation/depression, and spike‐timing‐dependent plasticity (STDP). When integrated into a spiking neural network (SNN) for digital image recognition using the MNIST dataset, the device achieves an accuracy of 86.68%. This work demonstrates the potential of the cost‐effective Ag/Bi bilayer electrode in enhancing the stability and performance of perovskite memristors.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-01-10
DOI
https://doi.org/10.1002/adfm.202420547
Citations
18
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
13.15

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article

Stable Halide Perovskite Memristor Utilizing Innovative Silver/Bismuth Electrode as an Alternative to Gold

Yanwei Fan, Qing Gong Song, Wei Cheng Huang, Deli Li et al.
18 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
13.15
article

Stable Halide Perovskite Memristor Utilizing Innovative Silver/Bismuth Electrode as an Alternative to Gold

Yanwei Fan, Qing Gong Song, Wei Cheng Huang, Deli Li, Yang Liu, Yue Wang, Yonghua Chen, Jiuchao Feng
article en
18 citations

Abstract

Abstract Perovskite memristors hold great promise for neuromorphic computing due to their ease of fabrication and sensitivity to light and electrical stimuli. However, the common use of expensive metal electrodes, such as gold (Au) and unstable silver (Ag), limits their stability and broader application. In this study, a cost‐effective perovskite memristor utilizing a novel Ag/Bismuth (Ag/Bi) bilayer electrode, which serves as a viable alternative to Au while maintaining excellent performance, is presented. This design prevents electrochemical reactions and the formation of unstable metallic filaments, enabling controlled mixed electronic/ionic conductivity. Moreover, the low work function of the Ag/Bi bilayer reduces the built‐in voltage, facilitating the formation and retention of conductive filaments, which improves device performance and stability. The memristor exhibits a high on/off ratio (10 2 ), excellent endurance (≈800 cycles), long retention (>10 4 s), and storage stability comparable to Au‐based devices. Furthermore, it demonstrates neuromorphic synaptic behaviors, including long‐ and short‐term plasticity (STP), potentiation/depression, and spike‐timing‐dependent plasticity (STDP). When integrated into a spiking neural network (SNN) for digital image recognition using the MNIST dataset, the device achieves an accuracy of 86.68%. This work demonstrates the potential of the cost‐effective Ag/Bi bilayer electrode in enhancing the stability and performance of perovskite memristors.

Advanced Functional MaterialsVol. 35(24)
Fujian Normal University (CN), Nanjing Tech University (CN), Northwestern Polytechnical University (CN), Nanjing University of Posts and Telecommunications (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
13.15
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