Linearity and intermodulation distortion analysis of single and dual metal gate junctionless transistor.

Linearity and intermodulation distortion are very crucial parameters for RFICs design. Therefore, in this work, a detailed comparative analysis on linearity and intermodulation distortion of single metal (SMG) and double metal (DMG) double gate junction less transistor (JLT) is done using TCAD silvaco suite. Furthermore, the effects of temperature fluctuation, gate length variation, and gate material engineering on the linearity performance of both devices are also studied. A few significant figures of merit, including Voltage Intercept Point 2 (VIP2), Voltage Intercept Point 3 (VIP3), Third Order Intercept Power (IIP3), 1 dB Compression Point (P1dB), Third Order Intermodulation Distortion (IMD3), and the transconductance derivative parameters First Order Transconductance (gm1), Second Order Transconductance (gm2), and Third Order Transconductance (gm3) are used to assess the device linearity and intermodulation distortion of SMG and DMG JLT's. The findings show that higher VIP2, VIP3, IIP3, 1-dB compression point and lower gm3, IMD3 values are obtained for the SMG JLT device when compared to its counterpart DMG JLT. SMG JLT, which assures strong linearity and low distortion.

A machine learning and blockchain based secure and cost-effective framework for minor medical consultations.

With the ever-increasing awareness among people regarding their health, visiting a doctor has become quite common. However, with the onset of the COVID-19 pandemic, home-based consultations are gaining popularity. Nevertheless, the worries over privacy and the lack of willingness to assist patients by the medical professionals in the online consultation process have made current models ineffective. In this paper, we present an advanced protected blockchain-based consultation model for minor medical conditions. Our model not only ensures users' privacy but by incorporating a calculation model, it also offers an opportunity for consulting end-users to voluntarily take part in the consultation process. Our work proposes a smart contract based on machine learning to be implemented for the prediction of a score of a professional who consults based on various prioritized parameters. This is done by using word2vec and TF-IDF weighting to classify the question and cosine similarity scores for detailed orientation analysis. Based on this score, the patient is charged, and simultaneously, the responder is awarded ether. An incentivized method leads to more accessible healthcare while reducing the cost itself.