Nonvolatile resistive memory and synaptic learning using hybrid flexible memristor based on combustion synthesized Mn-ZnO

doi:10.1016/j.jmst.2021.09.007

Abstract

Abstract:

Memristors have been seen as promising candidates for next-generation nonvolatile memory and neuromorphic computing. Solution-processed memristors can offer many advantages, such as low-cost fabrication, large-area uniformity, and high versatility. However, the high annealing temperature of the solution process and unsatisfactory performance are impeding the advancement of flexible memristors. In this work, the combustion method was proposed to fabricate the Mn-ZnO-based memristor without a high-temperature process on the flexible substrate. The device demonstrates stable bipolar resistive switching behavior with ultralow switching voltage (-0.6 V/0.71 V), high on/off ratio (˃ 2 × 103), multilevel storage capability, and uniform resistance distribution (σ/µ = 18%/16%). The flexible device with a bend radius of 10 mm shows no obvious performance degeneration compared with the device in the flat state. Furthermore, tunable repetitively synaptic weight was achieved by the memristor. Synaptic plasticity functions, such as excitatory postsynaptic current, paired-pulse facilitation, and learning/forgetting behaviors, have been emulated. This work provides a simple and feasible strategy for fabricating the flexible high-performance memristor, with the expectation to facilitate the development of flexible electronics.

Key words: Memristor, Flexible, Combustion synthesis, Synaptic plasticity, Mn-ZnO

Qi Xue, Tao Hang, Jianghu Liang, Chun-Chao Chen, Yunwen Wu, Huiqin Ling, Ming Li. Nonvolatile resistive memory and synaptic learning using hybrid flexible memristor based on combustion synthesized Mn-ZnO[J]. J. Mater. Sci. Technol., 2022, 119: 123-130.

Figures/Tables 6

Fig 1. (a) Illustration of the flexible Ag/Mn-ZnO/ITO memristor on PEN substrate and (b) optical image. (c) Cross-sectional SEM image of the Ag/Mn-ZnO/ITO memristor. (d) SEM and (e) AFM images and (f) XRD pattern of the Mn-ZnO films prepared on ITO substrate. (g) XPS survey and (h) O1s spectrum of the Mn-ZnO films. (i) PL spectra of the Mn-ZnO and RF-ZnO films.

Fig 2. The electronic characteristics of the Ag/Mn-ZnO/ITO device under the flat state. (a) A schematic illustration of the memristor. (b) Typical I-V curve of the device. (c) Conduction mechanism of the positive part of I-V curve. (d) I-V curves of the device under 50 consecutive DC sweeps. (e) Histograms of the set and reset voltage distribution. (f) Statistical cumulative probability of the on and off current.

Fig 3. Electronic characteristics of the Ag/Mn-ZnO/ITO device under bend state. (a) Schematic illustration of the memristor. (b) Typical I-V curve of the device. (c) Conduction mechanism of the positive part of I-V curve. (d) I-V curves of the device under 50 consecutive DC sweeps. (e) Histograms of the set and reset voltage distribution. (f) Statistical cumulative probability of the on and off current.

Fig 4. (a) I-V curves of the memristor under 0.1, 0.3, 0.5, 0.8, and 1 mA compliance current. (b) Resistance value of LRS with varied compliance current. (c) Schematic illustration of multilevel storage mechanism for Ag/Mn-ZnO/ITO device.

Fig 5. (a) Schematic diagram of the human neuronal synapse. (b) I-V curves of the memristor under 10 consecutive positive sweeping voltages and 10 negative sweeping voltages. (c) Current and voltage data vs time for the memristor. (d) The conductance was modulated for 10 cycles obtained by alternating positive and negative pulse trains. (e) The PPF effect obtained by the applied two paired pulses (2 V, 50 ms) at the interval (500 ms). (f) PPF ratio as a function of pulse interval.

Fig 6. Learning and memorization behavior of the Mn-ZnO-based memristor. (a) The photograph of the memristor array. (b) Initial state. (c) Synaptic weights after training 5 times. (d) Synaptic weights after training 50 times. (e) Synaptic weights after training 1000 s.

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