Modulation of Phase-Locking Characteristics of NbO<sub>x</sub> Memristor by Ag Doping

Memristors based on niobium oxide have attracted wide interest due to their applications in artificial neural network. Phase-locking (PL) characteristics of NbOx newly explored roots in complex nonlinear dynamics and exhibits great potential in periodical signal handling because of its bioinspired nature. In this study, we fabricate a Pd/Nb/Ag-NbOx/Nb/Pd memristive structure and study the effects of Ag doping on the PL characteristics. We clearly see that the NbOx memristor responds to signal in three distinct patterns. For the low-frequency input, the memristor fires spike series and locks the phase near π2 during the positive period of sinusoidal input, and it encodes the input features by the spike number per period. For the middle-frequency input, the memristor fires one spike per period at a definite phase. The output has the same frequency as the input. The locked phase is proportional to the input frequency. For the high-frequency input, the memristor transforms high frequency to low frequency signal and locks at a definite phase higher than 2π. We combined the microstructural analysis and chaos dynamics to know that Ag doping will lower the activation energy, enhance the responding rate, and enhance thermal conductivity, which extends the locked frequency to 1.7 times the value of the undoped NbOx memristor. We also obtained the locked frequency of even 40 MHz after modulating the elementary circuit configuration according to the material modification and chaos model. Our study proposes to handle a signal with high efficiency resembling auditory sense and inspires a novel artificial intelligent computation prototype.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2024-10-25
DOI
https://doi.org/10.1021/acsami.4c12956
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.04

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article

Modulation of Phase-Locking Characteristics of NbOx Memristor by Ag Doping

Feng Pan, Junwei Yu, Fei Zeng, Ziao Lu et al.
5 citations
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
1.04
article

Modulation of Phase-Locking Characteristics of NbOx Memristor by Ag Doping

Feng Pan, Junwei Yu, Fei Zeng, Ziao Lu, Da Ma, Qin Wan
article en
5 citations

Abstract

Memristors based on niobium oxide have attracted wide interest due to their applications in artificial neural network. Phase-locking (PL) characteristics of NbOx newly explored roots in complex nonlinear dynamics and exhibits great potential in periodical signal handling because of its bioinspired nature. In this study, we fabricate a Pd/Nb/Ag-NbOx/Nb/Pd memristive structure and study the effects of Ag doping on the PL characteristics. We clearly see that the NbOx memristor responds to signal in three distinct patterns. For the low-frequency input, the memristor fires spike series and locks the phase near π2 during the positive period of sinusoidal input, and it encodes the input features by the spike number per period. For the middle-frequency input, the memristor fires one spike per period at a definite phase. The output has the same frequency as the input. The locked phase is proportional to the input frequency. For the high-frequency input, the memristor transforms high frequency to low frequency signal and locks at a definite phase higher than 2π. We combined the microstructural analysis and chaos dynamics to know that Ag doping will lower the activation energy, enhance the responding rate, and enhance thermal conductivity, which extends the locked frequency to 1.7 times the value of the undoped NbOx memristor. We also obtained the locked frequency of even 40 MHz after modulating the elementary circuit configuration according to the material modification and chaos model. Our study proposes to handle a signal with high efficiency resembling auditory sense and inspires a novel artificial intelligent computation prototype.

ACS Applied Materials & InterfacesVol. 16(44)
Advanced Energy Materials (United States) (US), Tsinghua University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
1.04
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