Coexistence of short- and long-term memory in NbO<sub><i>x</i></sub>-based memristor for a nonlinear reservoir computing system

doi:10.15302/frontphys.2025.014208

Front. Phys.

2025, Vol. 20

Issue (1) : 14208 https://doi.org/10.15302/frontphys.2025.014208

Coexistence of short- and long-term memory in NbO_x-based memristor for a nonlinear reservoir computing system

Heeseong Jang, Jungang Heo, Jihee Park, Hyesung Na, Sungjun Kim(

)

Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea

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Abstract

In this study, TiN/NbO_x/Pt memristor devices with short-term memory (STM) and self-rectifying characteristics are used for reservoir computing. The STM characteristics of the device are detected using direct current sweep and pulse transients. The self-rectifying characteristics of the device can be explained by the work function differences between the TiN and Pt electrodes. Furthermore, neural network simulations were conducted for pattern recognition accuracy when the conductance was used as the synaptic weight. The emulation of synaptic memory and forgetfulness by short-term memory effects are demonstrated using paired-pulse facilitation and excitatory postsynaptic potential. The efficient training reservoir computing consisted of all 16 states (4-bit) in the memristor device as a physical reservoir and the artificial neural network simulation as a read-out layer and yielded a pattern recognition accuracy of 92.34% for the modified National Institute of Standards and Technology dataset. Finally, it is found that STM and long-term memory in the device coexist by adjusting the intensity of pulse stimulation.

Keywords memristor resistive switching neural network reservoir computing

Corresponding Author(s): Sungjun Kim

Just Accepted Date: 03 September 2024 Issue Date: 14 October 2024

Cite this article:

Sungjun Kim,Hyesung Na,Jihee Park, et al. Coexistence of short- and long-term memory in NbO_x-based memristor for a nonlinear reservoir computing system[J]. Front. Phys. , 2025, 20(1): 14208.

URL:

https://academic.hep.com.cn/fop/EN/10.15302/frontphys.2025.014208
https://academic.hep.com.cn/fop/EN/Y2025/V20/I1/14208

Fig.1 (a) Schematic of TiN/NbO_x/Pt device. (b) Transmission electron microscopy image and (c) energy dispersive spectroscopy weight percentage composition. X-ray photoelectron spectroscopy spectra of (d) Nb 3d and (e) O 1s for etching 0 s and 80 s.

Fig.2 (a) Direct current (DC) I–V curves of TiN/NbO_x/Pt device for 100 cycles. (b) The first and the second read currents after the 100^th cycle. (c) DC endurance at a read voltage of ?1 V. (d) Retention performance at a read voltage of ?1 V.

Fig.3 Schematic of oxygen vacancy model in the TiN/NbO_x/Pt device. (a) Pristine condition, (b) set process, and (c) reset process. (d, e) Energy band diagrams with self-rectifying properties.

Fig.4 (a) Potentiation and depression curves at a read voltage of 1 V. (b) Potentiation and depression synaptic plasticity of memristor for 10 cycles. (c) Modified National Institute of Standards and Technology (MNIST) pattern identification using a deep neural network simulation framework. (d) Plot of the accuracy of a synaptic device pattern recognition across a span of 10 epochs.

Fig.5 (a) Paired-pulse facilitation (PPF) with two 4 V pulses applied by varying the interval between them. (b) Statistical distribution of PPF as a function of the interval time. (c) Stimulation of 10 consecutive electrical spikes at frequencies in the range of 2–200 Hz. (d) Plot of current change as a function of frequency.

Tab.1 Comparative performances of NbO_x-based memristive devices.

Fig.6 (a) Reservoir system comprising an output node, resistive random access memory devices, and a pulse train. (b, c) Sixteen states according to distinct pulse streams. (d) Use of 5 × 4 pixels to implement the number “2”. (e) Plot of pattern accuracy dependence as a function of the number of training epochs. (f) Confusion matrix derived from the pattern test inference results.

Fig.7 (a) Temporal variations of read current value following application of different pulses. (b) Variations in read current as a function of the pulse number at different pulse widths. (c) Temporal changes in synaptic weights at different pulse numbers at a pulse amplitude of ?4.8 V under the same conditions. (d) Schematic of the short-term memory (STM) and long-term memory (LTM) transition processes through the rehearsal learning process. (e) STM to LTM conversion according to pulse amplitude following the application of 100 pulses. (f) Accuracy of classification using the MNIST dataset.

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