Stationary random-access memory (SRAM) undergoes an expansion stage, to repel advanced process variation and support ultra-low power operation. Memories occupy more than 80% of the surface in today’s microdevices, and this trend is expected to continue. Metal oxide semiconductor field effect transistor (MOSFET) face a set of difficulties, that results in higher leakage current (Ileakage) at lower strategy collisions. Fin field effect transistor (FinFET) is a highly effective substitute to complementary metal oxide semiconductor (CMOS) under the 45 nm variant due to advanced stability. Memory cells are significant in the large-scale computation system. SRAM is the most commonly used memory type; SRAMs are thought to utilize more than 60% of the chip area. The proposed SRAM cell is developed with FinFETs at 16 nm knot. Power, delay, power delay product (PDP), Ileakage, and stationary noise margin (SNM) are compared with traditional 6T SRAM cells. The designed cell decreases leakage power, current, and read access time. While comparing 6T SRAM and earlier low power SRAM cells, FinFET-based 10T SRAM provides significant SNM with reduced access time. The proposed 10T SRAM based on FinFET provides an 80.80% PDP reduction in write mode and a 50.65% PDP reduction in read mode compared to MOSEFET models. There is an improvement of 22.20% in terms of SNM and 25.53% in terms of Ileakage.

ABSTRACT

In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared.