Sadiqul Alam Saimon

This research proposes an optimized set of parameters for a design of the 6-T (Six Transistor CMOS SRAM) SRAM cell using CNTFET technology. The CNTFET utilized a planar gate structure with multiple cylindrical conducting channels and high k (high dielectric constant) gate dielectric material on a substrate with a different dielectric. In addition, the Stanford CNTFET model has studied, which is organized hierarchically in three main levels to simulate a MOSFET- like CNFET device. The performance of the CNTFET-based (Carbon Nano Tube Field-Effect Transistor)  SRAM (Static Random Access Memory) cell has evaluated using three main criteria: Static Noise Margin (SNM), Critical Write Time, and Standby Power. The optimization is conducted for two different voltage levels: 0.9V and 0.7V. The SNM is calculated using HSPICE simulation and the obtained data has utilized to optimize the CNTFET SRAM cell by varying  chirality and channel length. Furthermore, a comparison of the three matrices for CNTFET, CMOS, and FinFET processes is conducted. The simulation results confirm that the CNTFET SRAM design is a significant improvement in Read SNM with superior writability and lower power consumption compared to CMOS and FinFET processes. Additionally, the simulation of SNM for a wide temperature range demonstrates that the CNTFET SRAM has a relatively stable response to temperature variations.

Demand Side Management (DSM) has an important role in helping consumers to make the correct choice on their energy usage to reduce the overall power demand, peak load demand, and have a better load profile. Energy conservation has always been an essential part of DSM. In this work, we have tried to visualize the load profile of a model house before and after using DSM methods. A model house has been simulated in Matlab/Simulink for this purpose. Three DSM methods were applied in the model house. Various key results such as total power consumption, 24-hour load profile, and total unit (kWh) consumption with and without applying DSM are presented from the simulation data. This study will help to grow awareness about the benefits of DSM and change our view on the efficient use of power among Bangladeshi consumers.

Employee churn is a notable nuisance for organizations to maintain a cost-effective position and brand strategy. This research has proposed an integrated approach to overcome this issue systematically. All employees in this study were divided into three categories using a combination of machine learning algorithms and Multi-Criteria Decision Making (MCDM) techniques. The techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytical Hierarchy Process (AHP) have been consolidated. The dataset's feature importance was retrieved, and AHP was used to determine the criteria that caused the most turnover. TOPSIS was given the derived criterion weights from AHP to rank every personnel according to their propensity to depart the organization. To estimate staff turnover, seven machine learning algorithms were applied. After comparing the results, the Random Forest algorithm produces the best accuracy for assessing employee churn.