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By understanding the potential benefits of Gate Stack Dual Material double gate MOSFET (DG MOSFET), this research aims to contribute to the investigation of its electrical characteristics with improved performance and dependability. A detailed investigation is conducted into the temperature dependent electrical properties at different temperatures. A detailed analysis is conducted on the enhanced gate controllability in lower technology nodes and the protection against short channel effects. Together with the DC performances, this research also analyzes the suggested device's analog performance. The device's performance matrix was presented for temperatures ranging from 70 K to 800 K. The results showsthe improvement the drain current, transconductance, and transconductance generation factor(TGF) with decrease in temperature.

Elpasolites are expected to be promising photovoltaic semiconductor alternatives to lead-based perovskite semiconductors. Concern over the stability and toxicity of lead halide perovskites has led to an increase in interest in the environmental friendly elpasolite Cs2BiAgI6 (Cesium Bismuth Silver Iodide). An extensive numerical analysis of the Cs2BiAgI6 based elpasolite solar cell (ESC) model with the standard n-i-p nanostructured planar architecture ITO/TiO2/Cs2BiAgI6/CIGS/NiO/Au is presented in this article. Numerous device parameters, such as temperature, series resistance, thickness and the defect density of the dual-graded light absorber layers, electron transport layer (ETL), and hole transport layer (HTL), have all been thoroughly studied. The device numerical modeling and simulation is carried out using the well-known SCAPS-1D simulation tools (version 3.3.10). Under AM1.5G illumination, achieved a Voc of 1.2277 V, Jsc of 34.342 mA/cm2, and fill factor of 86.25% with power conversion efficiency of 36.36% through parameter optimization, which is much higher than the highest reported values found in the literature.

The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI’s potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

The deadzone occurring in a phase-frequency detector (PFD) is a critical parameter that affects the performance of a phase-locked loop (PLL). Though a fixed-delay element reduces the deadzone, it creates an imbalance in the pulse-arrival time and among the up and down signals to the charge pump, which increases the phase noise in the output spectrum of the PLL. Therefore, in this work a new variable-delay element (VDE) is incorporated in the PFD to reduce the dead zone and consequently the phase noise of the PLL. The performance of the proposed PFD incorporated in PLL is analyzed using cadence virtuoso 90 nm CMOS technology, achieving a phase noise of −148.89 dBc/Hz at a frequency offset of 1 MHz, a lock time of 6.01 us, a power of 0.056 mW, and a dead zone of 110.5 ps, while operating at 3.5 GHz of frequency, making it suitable for 5G applications.