Resistive Switching Characteristic Improvement in a Single-Walled Carbon Nanotube Random Network Embedded Hydrogen Silsesquioxane Thin Films for Flexible Memristors

Min, Shin-Yi; Cho, Won-Ju

doi:10.3390/ijms22073390

Cited by 11 publications

(4 citation statements)

References 57 publications

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“…In contrast, for the HRS and LRS of memristors without SWCNT random networks, the R avg values were 1.69 × 10 3 Ω and 2.25 × 10 2 Ω, respectively, with corresponding SDs of 1.08 × 10 2 Ω and 6.34 Ω. Consequently, the RS memory window, which is defined as the minimum HRS (HRS min )/maximum LRS (LRS max ), and the uniform resistance distribution of the nanocomposite memristors with SWCNT random networks were 2.3 times that of devices without SWCNTs. This is because the embedded SWCNT random network affects the interface dynamics of the chitosan thin-film layer via the adsorption of metal ions [ 34 ], which affects its interface dynamics, resulting in a large memory window through stable multilevel RS operation [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

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

Min

Cho

2021

Micromachines

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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.

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Section: Resultsmentioning

confidence: 99%

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

Min

Cho

2021

Micromachines

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“…In memristors with counterclockwise switching, when the applied memristor voltage is larger than the positive threshold voltage V p , the resistance of the memristor decreases. On the contrary, in memristors with clockwise switching, applying a voltage larger than V p causes an increase in resistance, while the resistance decreases when the applied voltage is less than −| V n | (Min and Cho, 2021).With an applied voltage between −| V n | and V p , the resistance remains constant.…”

Section: Memristor Modelmentioning

confidence: 98%

Hardware Implementation of Differential Oscillatory Neural Networks Using VO 2-Based Oscillators and Memristor-Bridge Circuits

et al. 2021

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Oscillatory Neural Networks (ONNs) are currently arousing interest in the research community for their potential to implement very fast, ultra-low-power computing tasks by exploiting specific emerging technologies. From the architectural point of view, ONNs are based on the synchronization of oscillatory neurons in cognitive processing, as occurs in the human brain. As emerging technologies, VO2 and memristive devices show promising potential for the efficient implementation of ONNs. Abundant literature is now becoming available pertaining to the study and building of ONNs based on VO2 devices and resistive coupling, such as memristors. One drawback of direct resistive coupling is that physical resistances cannot be negative, but from the architectural and computational perspective this would be a powerful advantage when interconnecting weights in ONNs. Here we solve the problem by proposing a hardware implementation technique based on differential oscillatory neurons for ONNs (DONNs) with VO2-based oscillators and memristor-bridge circuits. Each differential oscillatory neuron is made of a pair of VO2 oscillators operating in anti-phase. This way, the neurons provide a pair of differential output signals in opposite phase. The memristor-bridge circuit is used as an adjustable coupling function that is compatible with differential structures and capable of providing both positive and negative weights. By combining differential oscillatory neurons and memristor-bridge circuits, we propose the hardware implementation of a fully connected differential ONN (DONN) and use it as an associative memory. The standard Hebbian rule is used for training, and the weights are then mapped to the memristor-bridge circuit through a proposed mapping rule. The paper also introduces some functional and hardware specifications to evaluate the design. Evaluation is performed by circuit-level electrical simulations and shows that the retrieval accuracy of the proposed design is comparable to that of classic Hopfield Neural Networks.

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“…Memristor electrodes not only carry electric current, but may also participate in the resistive reaction. They are commonly made from the following materials: conventional metals [ 27–31 ], noble metals [ 32 , 33 ], alloys [ 34–36 ], carbon-based materials such as graphene [ 37 ] and carbon nanotubes [ 38 ], nitrides such as TiN [ 16 ] and TaN [ 39 ], transparent conductive flexible oxides such as indium tin oxide (ITO) [ 29 , 39 , 40 ] and doped ITO [ 41 ], F-doped tin oxide (FTO) [ 42–46 ], etc. The common electrode materials can be classified into four types according to their different role in RS behavior.…”

Section: Materials Of Memristorsmentioning

confidence: 99%

A review of memristor: material and structure design, device performance, applications and prospects

Xiao

Jiang

Chen

et al. 2023

Science and Technology of Advanced Materials

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With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

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Resistive Switching Characteristic Improvement in a Single-Walled Carbon Nanotube Random Network Embedded Hydrogen Silsesquioxane Thin Films for Flexible Memristors

Cited by 11 publications

References 57 publications

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

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

Hardware Implementation of Differential Oscillatory Neural Networks Using VO 2-Based Oscillators and Memristor-Bridge Circuits

A review of memristor: material and structure design, device performance, applications and prospects

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