Chitosan-Based Flexible Memristors with Embedded Carbon Nanotubes for Neuromorphic Electronics.

Jin-Gi Min,Won-Ju Cho

doi:10.3390/mi12101259

Abstract

In this study, we propose high-performance chitosan-based flexible memristors with embedded single-walled carbon nanotubes (SWCNTs) for neuromorphic electronics. These flexible transparent memristors were applied to a polyethylene naphthalate (PEN) substrate using low-temperature solution processing. The chitosan-based flexible memristors have a bipolar resistive switching (BRS) behavior due to the cation-based electrochemical reaction between a polymeric chitosan electrolyte and mobile ions. The effect of SWCNT addition on the BRS characteristics was analyzed. It was observed that the embedded SWCNTs absorb more metal ions and trigger the conductive filament in the chitosan electrolyte, resulting in a more stable and wider BRS window compared to the device with no SWCNTs. The memory window of the chitosan nanocomposite memristors with SWCNTs was 14.98, which was approximately double that of devices without SWCNTs (6.39). Furthermore, the proposed SWCNT-embedded chitosan-based memristors had memristive properties, such as short-term and long-term plasticity via paired-pulse facilitation and spike-timing-dependent plasticity, respectively. In addition, the conductivity modulation was evaluated with 300 synaptic pulses. These findings suggest that memristors featuring SWCNT-embedded chitosan are a promising building block for future artificial synaptic electronics applications.

Highlights

Owing to the von Neumann bottleneck, conventional computing systems face enormous challenges when dealing with real-time decision-making processes and large amounts of unstructured data and processing large amounts of information [1]
Efficient, high-performance computing systems are considered the new benchmarks for rapid processing of big data [2,3]
Regarding the implementation of such computing systems, two-terminal memristors with metal–insulator–metal structures have been studied extensively owing to their geometric simplicity, nonvolatile memory, low operating power consumption, and ability to perform computations based on successive analog resistive switching (RS) in the insulating layer [4,5,6]

Summary

Introduction

Owing to the von Neumann bottleneck, conventional computing systems face enormous challenges when dealing with real-time decision-making processes and large amounts of unstructured data and processing large amounts of information [1]. Efficient, high-performance computing systems are considered the new benchmarks for rapid processing of big data [2,3]. Regarding the implementation of such computing systems, two-terminal memristors with metal–insulator–metal structures have been studied extensively owing to their geometric simplicity, nonvolatile memory, low operating power consumption, and ability to perform computations based on successive analog resistive switching (RS) in the insulating layer [4,5,6]. Various materials, including bio-inspired, organic, inorganic, and hybrid nanocomposites, have been explored for the RS layer of memristors. Among bio-inspired organic materials, Micromachines 2021, 12, 1259.

Methods

Results

Conclusion