Stability improvement of an efficient graphene nanoribbon field-effect transistor-based sram design

The development of the nanoelectronics semiconductor devices leads to the shrinking of transistors channel into nanometer dimension. However, there are obstacles that appear with downscaling of the transistors primarily various short-channel effects. Graphene nanoribbon field-effect transistor (GNRFET) is an emerging technology that can potentially solve the issues of the conventional planar MOSFET imposed by quantum mechanical (QM) effects. GNRFET can also be used as static random-access memory (SRAM) circuit design due to its remarkable electronic properties. For high-speed operation, SRAM cells are more reliable and faster to be effectively utilized as memory cache. The transistor sizing constraint affects conventional 6T SRAM in a trade-off in access and write stability. This paper investigates on the stability performance in retention, access, and write mode of 15 nm GNRFET-based 6T and 8T SRAM cells with that of 16 nm FinFET and 16 nm MOSFET. The design and simulation of the SRAM model are simulated in synopsys HSPICE. GNRFET, FinFET, and MOSFET 8T SRAM cells give better performance in static noise margin (SNM) and power consumption than 6T SRAM cells. The simulation results reveal that the GNRFET, FinFET, and MOSFET-based 8T SRAM cells improved access static noise margin considerably by 58.1%, 28%, and 20.5%, respectively, as well as average power consumption significantly by 97.27%, 99.05%, and 83.3%, respectively, to the GNRFET, FinFET, and MOSFET-based 6T SRAM design. © 2020 Mathan Natarajamoorthy et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

The effect of forest area change in tropical islands towards baseflow and streamflow

Baseflow is a very important component in maintaining river flow. Baseflow is generated from groundwater flow. Baseflow is very important role in water conservation. Indonesia is located in a tropical climate. Based on the Koppen climate classification, Indonesia is included in the tropical rainforest climate (type Af), so that the characteristics of the climate, soil, flora and fauna are unique. About 70% of Indonesia's territory consists of water. Baseflow in the tropical island region has a unique characteristic. This research aimed to obtain the knowledge on the effect of forest area change in tropical islands towards the characteristics of baseflow and streamflow that involved RDF filter parameter calibration, baseflow contribution towards streamflow based on the Baseflow Index (BFI) and Q90 on Q50 ratio, rain contribution on the total baseflow, baseflow stream domination, and discharge stability of streamflow. This research was conducted in three sub watersheds with forest area percentage of ±18% Bango Sub watershed, ±23% Brantas Hulu Sub watershed, and ±59% Cobanrondo Sub watershed. The results showed that One Parameter Algorhitm method satisfied the application of baseflow separation. There was a positive correlation between rain and baseflow. Baseflow dominated the streamflow in the research location. The greater the forest area, the more stable the flow condition from year to year

A real-time pothole detection based on deep learning approach

Today, the number of vehicles using the road including highways and single carriage way is increasing. road structure safety monitoring system that is safe for road users and also important to ensure long-term vehicle safety and prevent accidents due to road damage such as potholes, landslides and uneven roads. Most news reports of road accidents are also caused by potholes that are almost 10-30 cm deep, coupled with heavy rainfall that reduces visibility among drivers, significant damage to the suspension system to the vehicle or unnecessary traffic congestion. In this paper, deep learning detection with YOLOv3 algorithm is proposed apart from researches ranging from accelerometer detection, image processing or machine learning based detection as it is easier to develop and provide more accurate results. After pothole has been detected in real-time webcam, the location will be logged and displayed using Google Maps API for visualization. a total of 330 sets of data were sampled for the implementation of the pothole detection training model. As the results, the model provided 65.05 mAP and 0.9 % precision rate and 0.41 recall rate. The limitation of YOLOv3 algorithm detection can be improve further using GPU with higher specification performances and can sample 1000 to 10,000 datasets. The proposed algorithm provides acceptably high precision and efficient pothole monitoring solution under different scenarios for the users and may benefit the public and the government to monitor pothole in real-time

Trend analysis of droughts during crop growing seasons of Nigeria

This study assesses the impacts of recent climate changes on drought-affected areas and the occurrence of droughts during different cropping seasons of Nigeria using the standardized precipitation evapotranspiration index (SPEI). The crop growing seasons are considered because the droughts for those periods are more destructive to national agricultural production. The Mann-Kendall test and binary logistic regression were used to quantify the trends in drought-affected areas and the occurrence of crop droughts with different areal extents, respectively. Gauge-based gridded rainfall and temperature data for the period 1961-2010 with spatial resolutions of 0.5° were used. Results showed an increase in the areal extent of droughts during some of the cropping seasons. The occurrences of droughts, particularly moderate droughts with smaller areal extents, were found to increase for all of the seasons. The SPEI values calculated decreased mostly in the regions where rainfall was decreasing. That is, the recent changes in climate were responsible for the increase in the occurrences of droughts with smaller areal extents. These trends in climate indicate that the occurrence of larger areal extent droughts may happen more frequently in Nigeria in the future

Electrode-Less Photo-Assisted Etching Of P-Type And N-Type GaN

Poor light extraction due to total internal reflection (TIR) phenomenon has become a major problem in InGaN/GaN light-emitting diode (LED) [1-2]. Surface texturing by photoelectrochemical (PEC) etching gives access to more than 70% improvement in light extraction as reported in [3]. In this work, an electrode-less photo-assisted etching is demonstrated on Ga-polar face of n-type and p-type GaN layers. Two type of etchant solutions, H3PO4 with KOH and H3PO4 with HNO3 were used and the surface morphology of all samples were measured using scanning electron microscopy (SEM). Hexagonal pit on the surface of all samples were observed. Interestingly, the pits were formed in various uniformity, density and size depending on the type of solution. Surface roughness of etch samples is improved after etching as measured using atomic force microscopy (AFM)

Performance analysis of an efficient montgomery multiplier using 7nm FinFET and junctionless FinFET

The digital multipliers are the assertive sources of power exhaustion in the modern digital systems. To perform most efficient arithmetic based calculations, Montgomery multiplication can be one of the best alternatives for other conventional methods in digital architecture as high-speed multipliers are desired for its remarkable performance. The main drawback of the digital multipliers is that power exhaustion is very high when compared to the other elements of the digital circuit. Shift register is the one of the most important component in a digital multiplier which consumes comparatively higher power than the other components. Shift registers contains a series of D-flip flops to store the digital data. In order to obtain a notable improvement in terms of power consumption at the chip level, the flip-flop can be modified to achieve the reduction of average power in the multiplier. The Fin-Field Effect Transistor (FinFET) is a promising candidate to overcome fundamental limitations of its Silicon based alternative MOSFET. However, there seems to be an increase in leakage power and delay. The Junctionless FinFET with uniform doping in the channel proves to offer a better performance in terms of overall speed, power consumption and power delay product. The architecture has been designed in 7nm FinFET and JL-FinFET in Synopsys HSpice and Silvaco TCAD. The results of the Montgomery Multiplier affirms that the overall energy is improved by 55% and speed of the device by 35% as compared to the existing Montgomery Multiplier

Hadamard transform improvement for hevc using intel avx-512

High Efficiency Video Coding (HEVC) doubles the data compression ratio compared to previous generation compression technology, Moving Picture Expert Group-Advanced Video Codec (MPEG-AVC/H.264) without sacrificing the image quality. However, this superior compression comes at the cost of more computation payload resulting in longer time for encoding and decoding. This work proposes the vectorization on HEVC data heavy computation algorithm, Hadamard Transform or Sum of Absolute Transform Difference (SATD) and Sum of Absolute Difference (SAD) to achieve optimized compression performance. Single Instruction Multiple Data (SIMD) acceleration will be based on the Intel AVX-512 (Advanced Vector Extension) Instruction Set Architecture (ISA). Since HEVC supports more coding tree block (CTB) sizes, SATD and SAD algorithms eventually become more complex compared to AVC. As a result, SATD and SAD algorithms with various block sizes will be subjected to SIMD acceleration. We provide performance evaluation based on different SIMD ISA and without SIMD implementation on HEVC SATD and SAD and found that AVX-512 optimized implementation performed faster when compared to non- optimized SATD and SAD but showed signs of reduced performance when compared to SSE optimized SATD and SAD

A novel application of Lobatto iiia solver for numerical treatment of mixed convection nanofluidic model

The objective of the current investigation is to examine the influence of variable viscosity and transverse magnetic field on mixed convection fluid model through stretching sheet based on copper and silver nanoparticles by exploiting the strength of numerical computing via Lobatto IIIA solver. The nonlinear partial differential equations are changed into ordinary differential equations by means of similarity transformations procedure. A renewed finite difference based Lobatto IIIA method is incorporated to solve the fluidic system numerically. Vogel's model is considered to observe the influence of variable viscosity and applied oblique magnetic field with mixed convection along with temperature dependent viscosity. Graphical and numerical illustrations are presented to visualize the behavior of different sundry parameters of interest on velocity and temperature. Outcomes reflect that volumetric fraction of nanoparticles causes to increase the thermal conductivity of the fluid and the temperature enhances due to blade type copper nanoparticles. The convergence analysis on the accuracy to solve the problem is investigated viably though the residual errors with different tolerances to prove the worth of the solver. The temperature of the fluid accelerates due the blade type nanoparticles of copper and skin friction coefficient is reduced due to enhancement of Grashof Number

Efficiency Droop Of InGaN/GaN Led With Different Indium Composition

III-nitride light emitting diodes (LEDs) have attracted considerable attraction due to their various applications in displays and illumination lighting. Nevertheless, the majority of InGaN/GaN LEDs suffer from the efficiency droop. This droop would limit the potential of the LEDs in high current applications. As widely reported, high indium content in InGaN/GaN multiquantum well active region of the LED promotes indium fluctuation that degrades the efficiency of the LED. In this work, we will present results of the efficiency droop for InGaN/GaN LED with indium content of 18% and 8%, respectively. The efficiency droop of the LED with 18% of indium shows higher efficiency droop than the LED with 8% of indium content

Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios

Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010–2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall