This work demonstrates a real-time visible-light phototransistor comprised of a wide-band-gap amorphous indium-gallium-zinc-oxide ͑a-IGZO͒ thin-film transistor ͑TFT͒ and a narrow-band-gap polymeric capping layer. The capping layer and the IGZO layer form a p-n junction diode. The p-n junction absorbs visible light and consequently injects electrons into the IGZO layer, which in turn affects the body voltage as well as the threshold voltage of a-IGZO TFT. The hysteresis behavior due to the charges at IGZO back interface is also discussed.
In this study, we propose a floating dual gate (FDG) indium-gallium-zinc-oxide (IGZO) thin film transistor (TFT) with a floating metal back gate that is directly contact with IGZO without a dielectric layer. The floating back gate effect is investigated by changing the work function (ϕ) of the back gate. The FDG IGZO TFT exhibits an improved field-effect mobility (μ), unchanged subthreshold swing (SS), high on/off current ratio, and a tunable threshold voltage ranged (Vth) from −5.0 to +7.9 V without an additional back gate power supply.
Abstract:The development of financial technology has fostered a wave revolutionary change in financial service industry worldwide. The objective of this study is to examine whether the adoption of Fintech contributes to the productivity growth of the Taiwan banking industry in 2015. To investigate such potential effect, the study use the preferred Cost Malmquist Index to estimate the 25 listed sample banks over the period from 2010 to 2015. The empirical result suggests the observed period 2014-2015, the ΔPTE, ΔAE, ΔPE, and ΔCSE are improving by 0.01%, 0.13%, 0.42%, and 0.10% respectively. Moreover, the degree of ΔT deterioration is much improved relative to the other observe periods without adoption of Fintech. It provides the positive evidence to support an adoption of Fintech contribute a potential growth of competiveness of the Taiwan banking industry.
Efficient image search clustering is prominent for image search engines for exponentially growing photo collections. In this work, we propose an image search clustering approach which selects multiple canonical images from image search results and constructs image clusters in real time on an image subgraph for the search results. The efficiency is achieved with the help of offline-computed image context graphs by distributed computing methods. Extending our prior works, we demonstrate the results of the proposed canonical image selection and preliminary outcomes of large-scale image graph construction in this proposal. We experiment in Flickr550 dataset, containing 540,321 Flickr photos.
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