Intrinsically Stretchable Threshold Switching Memristor for Artificial Neuron Implementations

doi:10.15541/jim20220712

Abstract

Abstract:

The exploration of flexible electronic devices with information processing functions of biological neurons is of great significance for the development of intelligent wearable technologies. Due to lack of inherent mechanical flexibility, conventional threshold-switching memristor based on rigid materials that can implement the computing functions of biological neurons is difficult to fulfill the requirements for potential applications in the future. In this work, an intrinsically stretchable threshold-switching memristor was prepared by using silver nanowire-polyurethane composite as the dielectric layer and liquid metal as the electrodes, respectively. Under application of a sweeping voltage, the device exhibited reliable threshold switching characteristics, which was switched from the high resistance state (HRS) to the low resistance state (LRS) during device programming and spontaneously relaxed to the HRS upon voltage application. Further analysis shows that the underlying mechanism can be attributed to the dynamic formation and rupture of discontinuous silver conductive filaments formed between silver nanowires. In the pulse programming mode, memristor device is able to emulate the integration and firing characteristics of biological neurons, suggesting its great potential as an artificial neuron. Moreover, the pulse amplitude and pulse interval modulated neuronal spiking behaviors are successfully replicated using such devices. Under 20% tensile strain, the threshold-switching memristor shows negligible changes in the operating parameters during device switching and neuronal function implementations, suggesting its excellent mechanical flexibility and stability. This work provides important guidelines for the development of high-performance stretchable artificial neuronal devices and next-generation intelligent wearable systems.

Key words: neuromorphic computing, memristor, threshold switching, stretchable, artificial neuron

CLC Number:

TN389

TIAN Yu, ZHU Xiaojian, SUN Cui, YE Xiaoyu, LIU Huiyuan, LI Runwei. Intrinsically Stretchable Threshold Switching Memristor for Artificial Neuron Implementations[J]. Journal of Inorganic Materials, 2023, 38(4): 413-420.

Figures/Tables 7

Fig. 1 Flow chart of Cu@GaIn/AgNWs-PU/Cu@GaIn device fabrication

Fig. 2 I-V characteristics of the Cu@GaIn/AgNWs-PU/Cu@GaIn device (a) I-V curve of the Cu@GaIn/AgNWs-PU/Cu@GaIn device; (b) Cumulative distribution function of the operation voltages; (c) I-V curves of the device under different compliance currents; (d) Dependence of the operation voltage on the thickness of the AgNWs-PU film

Fig. 3 Working mechanism of the Cu@GaIn/AgNWs-PU/Cu@GaIn device (a) Dependence of the device resistance at the LRS on the compliance currents; (b) Illustration of the dynamic Ag filament formation/rupture between AgNWs during threshold switching process

Fig. 4 Emulation of the integrate-and-fire behaviors of biological neurons with the Cu@GaIn/AgNWs-PU/Cu@GaIn device (a, b) Schematic diagram for (a) biological neuron and (b) artificial neuron; (c) Typical integrate-and-fire behavior of the memristor based artificial neuron; Colorful figures are available on website

Fig. 5 Influences of the voltage pulse amplitude and interval on the integrate-and-fire behaviors of the memristor based artificial neuron (a) Integrate-and-fire behaviors of the device as a function of the voltage pulse amplitude with pulse interval and width at 30 ms and 10 ms, respectively; (b) Relationship between the required pulse number for device firing (NFire) and the pulse amplitude; (c) Integrate-and-fire behaviors of the device as a function of the voltage pulse interval with pulse amplitude and width at 6 V and 10 ms, respectively; (d) Relationship between the required pulse number for device firing (NFire) and the pulse interval; Colorful figures are available on website

Fig. 6 Threshold switching voltages of the Cu@GaIn/AgNWs-PU/Cu@GaIn device under different tensile strain conditions (a) Schematics of the device stretching under tensile strain; (b) Optical images of the device before and after being stretched by 20%; (c, d) Evolution of the operation voltage for the device with tensile strain in (c) x and (d) y directions

Fig. 7 Integrate-and-fire function test of artificial neuron under tensile strain conditions (a) Control pulse interval (30 ms), width (10 ms) and amplitude (6 V) being unchanged, and the NFire change of the device by changing tensile strain ratio of the device in the x direction; (b) Evolution of NFire of the device with tensile strain ratio

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