Research

This research presents a major improvement in ultra-small (sub-5 nm) transistors using 2D materials called monolayer α-CS and SiP. By adding a thin underlayer (2 nm), the performance improves greatly—boosting the on/off current ratio up to 1000× for α-CS and 10,000× for SiP, and reducing the subthreshold swing to 108 mV/dec and 101 mV/dec. These results go beyond the targets set by ITRS 2028 for future high-performance electronics.

Methodology:

The study used the QuantumATK simulation tool to model and analyze the transistors. It combined Density Functional Theory (DFT) and Non-Equilibrium Green’s Function (NEGF) methods to calculate electronic structure and current flow. Devices were tested at different gate lengths, with and without the underlayer, to measure key factors like current ratio, switching speed, and energy use—proving that underlayer modulation greatly improves performance at nanoscale.

This study presents a high-gain, low-noise Op-Amp optimized for biomedical signals. Using a PMOS-driven folded-cascode input and Class-AB output stage, the design achieves 127.3 dB gain, high PSRR/CMRR, and excellent noise performance, making it ideal for sensing small, low-frequency biopotentials.

Methodology:

Designed and simulated using Cadence Virtuoso, the circuit includes a novel biasing scheme for improved PSRR and stability. The layout used common centroid and interdigitated techniques to minimize mismatch and noise. Post-layout analysis under different PVT conditions confirmed strong performance across metrics like gain, phase margin, and power efficiency.

This research proposes a deep learning model for detecting and locating faults in power transmission lines. Using a Deep Neural Network (DNN), the system achieves 98.775% accuracy in identifying fault types and locates the fault within ±2% of the line in over 80% of cases, offering a fast and cost-effective solution for grid reliability.

Methodology:

The authors used MATLAB Simulink with Simscape Electrical to simulate a 4-bus, 3-line system and create training data. RMS values of three-phase currents and voltages were extracted from fault events. Two DNN models—one for fault type, another for location—were developed in Python using the Adam optimizer and evaluated using classification accuracy and mean absolute error. The method proves highly accurate for real-world smart grid applications.