A stacked double gate junctionless tunnel field-effect transistor (JL-TFET) has been proposed and examined the effects of interface trap charges (ITCs) by introducing both acceptor and donor charges at the semiconductor/insulator interface. The structure uses two isolated gates (polarity gate and control gate) over an n-type-doped silicon substrate to function as a TFET. The effect of ITCs has been analysed in terms of DC and analogue/radio-frequency performance using parameters such as transfer characteristics, electric field, electric potential, transconductance (g m) for both conventional and gate stacked JL-TFET. Additionally, they have also analysed metrics used to measure the device linearity performance and intermodulation distortion such as higher-order transconductance coefficients (g m2 , g m3) and figure of merit. All the simulations have been performed with the help of an Atlas device simulator.

Temperature sensitivity is one of the major concern in conventional stacked gate-oxide junctionless tunnel-field-effect transistor (SGO-JL-TFET). In this regard, the authors have investigated the sensitivity toward the temperature variation of the SGO-JL double-gate TFET with low work-function live strip (LWLS-SGO-JL-TFET) and without LWLS-SGO-JL-TFET (SGO-JL-TFET). Furthermore, they have analysed and compared the impact of operating temperature variation on the DC, analogue/ radiofrequency and linearity performances of both the devices with the help of simulation results obtained using technology computer-aided design tool. It can be stated that the proposed device is less sensitive toward the temperature variation in terms of carrier concentration, electric field, on-state current and off-state current, as compared with conventional SGO-JL-TFET. Apart from these parameters, proposed device also demonstrates better temperature sensitivity in terms of analogue performance parameters such as transconductance (g m) cutoff frequency (f T), gain bandwidth product and maximum oscillating frequency (f max). Therefore, the proposed device can be a potential candidate for cryogenics and high-temperature applications.

No abstract

The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest Neighbour, Logistic Regression, Random Forest, Support Vector Machine (SVM), and Decision Tree. The suggested technique works better than conventional methods, providing a more automated and effective method of diabetes detection. It could transform diabetes diagnosis, enhance patient outcomes, and enable individualised treatment plans.