Memristive Devices from CuO Nanoparticles

Walke, Pundalik D.; Rana, Abu ul Hassan S.; Yuldashev, Sh. U.; Magotra, Verjesh Kumar; Lee, Dong Jin; Abdullaev, Shovkat; Kang, Tae Won; Jeon, H. C.

doi:10.3390/nano10091677

Cited by 6 publications

(6 citation statements)

References 29 publications

(35 reference statements)

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“…To explore the current transmission mechanism of the device, the I-V scan curve of the device was redrawn with double logarithmic coordinates under negative voltage, as shown in Figure 6 a–c. In a low resistance state (LRS), the slopes of the corresponding double logarithmic (I–V) curves of the device, Al/EA/ITO and Al/EA: (1.2 ωt%, 3 ωt%) MWCNTs/ITO, are 0.93, 0.99, and 0.99, respectively, which are all close to 1, and the conduction behaviour of carriers in this region obeys Ohm’s law [ 41 ]. In the low voltage range of the high resistance state (HRS), the corresponding slopes are 1.22, 1.17, and 1.04, and this region is represented as an ohmic conductive region.…”

Section: Resultsmentioning

confidence: 99%

Tunable Multilevel Data Storage Bioresistive Random Access Memory Device Based on Egg Albumen and Carbon Nanotubes

2021

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In this paper, a tuneable multilevel data storage bioresistive memory device is prepared from a composite of multiwalled carbon nanotubes (MWCNTs) and egg albumen (EA). By changing the concentration of MWCNTs incorporated into the egg albumen film, the switching current ratio of aluminium/egg albumen:multiwalled carbon nanotubes/indium tin oxide (Al/EA:MWCNT/ITO) for resistive random access memory increases as the concentration of MWCNTs decreases. The device can achieve continuous bipolar switching that is repeated 100 times per cell with stable resistance for 104 s and a clear storage window under 2.5 × 104 continuous pulses. Changing the current limit of the device to obtain low-state resistance values of different states achieves multivalue storage. The mechanism of conduction can be explained by the oxygen vacancies and the smaller number of iron atoms that are working together to form and fracture conductive filaments. The device is nonvolatile and stable for use in rewritable memory due to the adjustable switch ratio, adjustable voltage, and nanometre size, and it can be integrated into circuits with different power consumption requirements. Therefore, it has broad application prospects in the fields of data storage and neural networks.

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Section: Resultsmentioning

confidence: 99%

Tunable Multilevel Data Storage Bioresistive Random Access Memory Device Based on Egg Albumen and Carbon Nanotubes

2021

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“…However, some devices switch oppositely due to their device structure, charge transport mechanism, and RS mechanism 20 . In the literature, the negative differential resistance (NDR) effect was also demonstrated for copper oxide-based RS devices 21 . Considering the technology demand, the research should be directed towards lowering the switching voltages.…”

Section: Resultsmentioning

confidence: 99%

Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques

Patil

Kundale

Sutar

et al. 2023

Sci Rep

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In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.

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“…The method employed to fabricate a device will affect its RS properties [ 100 , 101 , 102 ], with the same material potentially demonstrating different RS behavior when synthesized using different approaches [ 103 , 104 ]. Thus, various factors such as industrial practicality, cost-effectiveness, and RS performance need to be considered when fabricating an RS device.…”

Section: Fabrication Techniques For Resistive Switching Devicesmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications

et al. 2023

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Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

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Memristive Devices from CuO Nanoparticles

Cited by 6 publications

References 29 publications

Tunable Multilevel Data Storage Bioresistive Random Access Memory Device Based on Egg Albumen and Carbon Nanotubes

Tunable Multilevel Data Storage Bioresistive Random Access Memory Device Based on Egg Albumen and Carbon Nanotubes

Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications

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