The design and characteristics of a junctionless (JL) bulk FinFET were compared with the silicon-on-insulator (SOI) JL nanowire transistor (JNT) using 3-D quantum transport device simulation. The JL bulk FinFET exhibits a favorable ON/OFF current ratio and short-channel characteristics by reducing the effective channel thickness that is caused by the channel/substrate junction. The drain-induced barrier lowering and the subthreshold slope are about 40 mV and 73 mV/dec, respectively, with an ON/OFF current ratio of 10 5 at W = 10 nm. The JL bulk FinFET is less sensitive to the channel thickness than the SOI JNT. Furthermore, the threshold voltage V th of the JL bulk FinFET can be easily tuned by varying substrate doping concentration N sub . The modulation range of V th as N sub changes from 10 18 to 10 19 cm −3 , which is around 30%. Index Terms-Fin-shaped field-effect transistor (FinFET), junctionless (JL), 3-D simulation.
Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.
This letter demonstrates for the first time junctionless (JL) gate-all-around (GAA) poly-Si thin-film transistors (TFTs) with ultrathin channels (2 nm). The subthreshold swing is 61 mV/decade and the ON/OFF current ratio is close to 10 8 because of the excellent gate controllability and ultrathin channel. The JL-GAA TFTs have a low drain-induced barrier lowering value of 6 mV/V, indicating greater suppression of the shortchannel effect than in JL-planar TFTs. The cumulative distribution of electrical parameters in JL-GAA is small. Therefore, the proposed JL-GAA TFTs of excellent device characteristics along with simple fabrication are highly promising for future system-on-panel and system-on-chip applications.Index Terms-Gate-all-around (GAA), junctionless (JL), thin-film transistor, ultrathin channel.
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