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Hf0.5Zr0.5O2-based ferroelectric memristor with multi-level storage potential and artificial synaptic plasticity

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  • ReceivedMar 13, 2020
  • AcceptedJun 29, 2020
  • PublishedSep 25, 2020

Abstract

Memristors are designed to mimic the brain’s integrated functions of storage and computing, thus breaking through the von Neumann framework. However, the formation and breaking of the conductive filament inside a conventional memristor is unstable, which makes it difficult to realistically mimic the function of a biological synapse. This problem has become a main factor that hinders memristor applications. The ferroelectric memristor overcomes the shortcomings of the traditional memristor because its resistance variation depends on the polarization direction of the ferroelectric thin film. In this work, an Au/Hf0.5Zr0.5O2/p+-Si ferroelectric memristor is proposed, which is capable of achieving resistive switching characteristics. In particular, the proposed device realizes the stable characteristics of multi-level storage, which possesses the potential to be applied to multi-level storage. Through polarization, the resistance of the proposed memristor can be gradually modulated by flipping the ferroelectric domains. Additionally, a plurality of resistance states can be obtained in bidirectional continuous reversibility, which is similar to the changes in synaptic weights. Furthermore, the proposed memristor is able to successfully mimic biological synaptic functions such as long-term depression, long-term potentiation, paired-pulse facilitation, and spike-timing-dependent plasticity. Consequently, it constitutes a promising candidate for a breakthrough in the von Neumann framework.


Funded by

the National Natural Science Foundation of China(61674050,61874158)

the Outstanding Youth Project of Hebei Province(F2016201220)

the Outstanding Youth Cultivation Project of Hebei University(2015JQY01)

the Project of Science and Technology Activities for Overseas Researcher(CL,201602)

the Project of Distinguished Young of Hebei Province(A2018201231)

the Support Program for the Top Young Talents of Hebei Province(70280011807)

the Training and Introduction of High-level Innovative Talents of Hebei University(801260201300)

the Hundred Persons Plan of Hebei Province(E2018050004,E2018050003)

and the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province(SLRC2019018)


Acknowledgment

This work was supported by the National Natural Science Foundation of China (61674050 and 61874158), the Outstanding Youth Project of Hebei Province (F2016201220), the Outstanding Youth Cultivation Project of Hebei University (2015JQY01), the Project of Science and Technology Activities for Overseas Researcher (CL 201602), the Project of Distinguished Young of Hebei Province (A2018201231), the Support Program for the Top Young Talents of Hebei Province (70280011807), the Training and Introduction of High-level Innovative Talents of Hebei University (801260201300), the Hundred Persons Plan of Hebei Province (E2018050004 and E2018050003), and the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province (SLRC2019018).


Interest statement

The authors declare that they have no conflict of interest.


Contributions statement

Yan X conceived the idea and revised the paper. Yu T fabricated the samples, finished the test data and prepared the manuscript. Chang J and Chen J coordinated this study. This article was discussed with contributions from all authors. All authors have approved the final version of this article.


Author information

Xiaobing Yan is currently a professor at the College of Electronic Information Engineering, Hebei University. He received his PhD degree from Nanjing University in 2011. From 2014 to 2016, he held the Research Fellow position at the National University of Singapore. His current research interest is in the field of memristors.


Tianqi Yu received a bachelor’s degree from Henan University of Science and Technology in 2018. He is a graduate student at the College of Electronic Information Engineering, Hebei University. His current research focuses on ferroelectric materials for memristor applications.


Supplement

Supplementary information

Experimental details are available in the online version of the paper.


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  • Figure 1

    Schematic of the device structure, PFM and TEM measurements of the HZO film. (a) Device structure diagram. (b) TEM image of the cross-section structure of the grown Au/HZO/P+-Si. (c) Morphological scan of HZO grown on P+-Si over a 10 μm × 10 μm area. (d) PFM hysteresis loop. (e) PFM amplitude image. (f) PFM phase image.

  • Figure 2

    I-V curves, resistance switching voltages, high and low resistance distributions of A- (a–d), B- (e–h) and C-type (i–l) devices. (a, e, i) Classic I-V curves in 50 cycles. (b, f, j) Distribution histograms and Gaussian fitting curves of set voltages. (c, g, k) Distribution histograms and Gaussian fitting curves of reset voltages. (d, h, l) Statistical high- and low-resistance profiles.

  • Figure 3

    Continuous regulatable and retentive testing of the devices. (a, c) The positive and negative portions of the I-V curves indicate that the device can simulate fluctuations in biological synapses, i.e., the continuously adjustable performance of the device. (b, d) Positive and negative regulatable resistance state retention testing.

  • Figure 4

    Characteristics and memristive behavior of the ferroelectric memristor devices. (a) Memristive behavior produced by the classic pulsed application of the device. Applying a negative pulse/positive pulse to the device causes the conductance to decrease/increase, representing synaptic weight modulation due to boosting or suppressing the pulse. (b) Typical R-V hysteresis loop measured by increasing the negative voltage pulse from −6 to 7 V. (c) Change in the resistance state when a pulse width of 1 ms, an interval of 1 ms, and a reset voltage of 6.5 V were applied to the device. (d) Change in the resistance state when a pulse width of 1 ms, an interval of 1 ms, and a reset voltage of −5 V were applied to the device. (e, f) The changes of resistance state of the Au/HZO/Si device with the pulse amplitude and pulse duration.

  • Figure 5

    Au/HZO/p+-Si device response to the application of an input pulse sequence. (a) A sequence of excitation pulses for applying different voltage amplitudes to the device, from 4.5 to 8 V, where the pulse width and spacing of the control pulses are kept at 1 ms. (b) For the action response, it shows the current response of the device after applying a pulse sequence of different voltage amplitudes, while observing the incubation time under different conditions (the number of pulse sequences required). The black arrow indicates the incubation time produced when a voltage amplitude of 6 V is applied to the device.

  • Figure 6

    Biological synaptic diagram and learning characteristics. (a) Schematic representation of the neuronal synaptic structures, including presynaptic, postsynaptic, cell and nucleus, axons and dendrites. (b) STDP characteristics of the ferroelectric memristors. As Δt increases, the ΔW of LTP and LTD gradually decreases. The green lines indicate the fitting results of the data. (c, d) Characteristic curves of PPF. As the time interval increases, the rate of change of its PPF gradually decreases. The green lines indicate the fitting results of the data.

  • Table 1   Statistics of this work and previous reports on dopants used for doping HfO2 thin films, characterization methods, device switching resistance ratio, and synaptic plasticity behavior

    Structure

    Dopant

    Characterization method

    Roff/Ron

    Synaptic plasticity

    Refs.

    Au/HZO/Si

    Zr

    TEM, PFM

    1500%

    Yes

    Our work

    TiN/HfO2/TiN

    No

    N. A.

    1000%

    Yes

    [29]

    Ti/HfO2/TiN

    No

    N. A.

    1000%

    Yes

    [34]

    Au/HZO/TiN

    Zr

    GIXRD

    166%

    No

    [41]

    TiN/HZO/TiN

    Zr

    HR-TEM

    N. A.

    N. A.

    [50]

    Ti/HfO2/TiN

    No

    N. A.

    N. A.

    Yes

    [45]

    TiN/HfO2/TiN

    No

    XRD

    1000%

    No

    [32]

    TiN/HLO/TiN

    La

    GIXRD

    N. A.

    N. A.

    [42]

    TiN/HZO/TiN

    Zr

    N. A.

    N. A.

    N. A.

    [14]

    Pt/HZO/TiN

    Zr

    GIXRD

    N. A.

    N. A.

    [37]

    TiN/Gd:HfO2/TiN

    Gd

    XRD

    N. A.

    N. A.

    [30]

    TiN/HfO2/TiN

    No

    XRD

    N. A.

    N. A.

    [33]

    TiN/HZO/Si

    Zr

    TEM

    800%

    Yes

    [46]