No abstract

As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.

In the proposed work, a differential write and single-ended read half-select free 12 transistors static random access memory cell is designed and simulated. The proposed cell has a considerable reduction in power dissipation with better stability and moderate performance. This cell operates in subthreshold region and has a higher value of read static noise margin as compared to conventional six transistors static random access memory cell. A power cut-off technique is utilized between access and pull-up transistors during the write operation. It results in an increase in write static noise margin as compared to all considered cells. In the proposed cell, read and write access time is improved along with a reduction in read/write power dissipation as compared to conventional six transistors static random access memory cell. The bitline leakage current in the proposed cell is reduced which improves the [Formula: see text] ratio of the cell under subthreshold region. The proposed cell occupies less area as compared to considered radiation-hardened design 12 transistors static random access memory cell. The computed electrical quality metric of proposed cell is better among considered static random access memory cells. Process variation analysis of read stability, access time, power dissipation, read current and leakage current has been performed with the help of Monte Carlo simulation at 3,000 points to get more soundness in the results. All characteristics of static random access memory cells are compared at various supply voltages.

In this paper, a 9T SRAM cell with low power (LP9T) and improved performance has been proposed. This cell is free from half-select issue and works with single-ended read and differential write operation in the sub-threshold region. To evaluate the relative performance, the obtained characteristics of LP9T SRAM cell are compared with other state-of-the-art designs at 45-nm technology node. The read and write power dissipation of LP9T SRAM cell is reduced by [Formula: see text] and [Formula: see text] as compared to Conv.6T SRAM cell. In proposed cell, leakage power is reduced by [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] as compared to conventional 6T (Conv.6T), low power (LP8T), transmission gate 8T(TG8T), transmission gate 9T (TG9T), Schmitt trigger 9T (ST9T), and positive feedback control 10T (PFC10T) SRAM cells. This reduction in leakage power is attributed to stacking effect. LP9T SRAM cell also exhibits significant improvement in read/write access time as compared to all considered cells. Also, the read and write energy of proposed cell is lowest among all considered cells. The LP9T SRAM cell has [Formula: see text] and [Formula: see text] higher read and write stability as compared to Conv.6T SRAM cell. Proposed SRAM cell has the highest value of ON to OFF current ratio ([Formula: see text]) which signifies the highest bit-cell density among all considered cells. The LP9T SRAM cell occupies [Formula: see text] large area as compared to Conv.6T SRAM cell. The overall quality of SRAM cell is calculated through the electrical quality metric (EQM). It is observed that LP9T SRAM cell has the highest value of EQM in comparison to considered cells at 0.3[Formula: see text]V supply voltage.

No abstract