The resistive switching characteristics of indium tin oxide (ITO)/Zn1−xCoxO/ITO transparent resistive memory devices were investigated. An appropriate amount of cobalt dopant in ZnO resistive layer demonstrated sufficient memory window and switching stability. In contrast, pure ZnO devices demonstrated a poor memory window, and using an excessive dopant concentration led to switching instability. To achieve suitable memory performance, relying only on controlling defect concentrations is insufficient; the grain growth orientation of the resistive layer must also be considered. Stable endurance with an ON/OFF ratio of more than one order of magnitude during 5000 cycles confirmed that the Co-doped ZnO device is a suitable candidate for resistive random access memory application. Additionally, fully transparent devices with a high transmittance of up to 90% at wavelength of 550 nm have been fabricated.

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This work investigates the effect of an in situ hydrogen plasma treatment on gate bias stability and performance of amorphous InGaZnO thin-film transistors (TFTs) deposited by using atmospheric-pressure PECVD. The H2 plasma-treated a-IGZO channel has shown significant improvement in bias stress induced instability with a minuscule threshold voltage shift (ΔV th) of 0.31 and −0.17 V under positive gate bias stress (PBS) and negative gate bias stress (NBS), respectively. With the aid of the energy band diagram, the proposed work demonstrates the formation of negative species O2 − and positive species H2O+ in the backchannel under PBS and NBS in addition to ionized oxygen vacancy (Vo) defects at a-IGZO/ZrO2 interfaces are the reason for gate bias instability which could be effectively suppressed with in situ H2 plasma treatment. From the experimental result, it is observed that the electrical performance such as field-effect mobility (μ FE), on-off current ratio (I on/I off), and subthreshold swing improved significantly by in situ H2 plasma treatment with passivation of interface trap density and bulk trap defects.

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a b s t r a c tIn this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems.