Advanced CMOS Process for Submicron Silicon Carbide (SiC) Device

Silicon carbide (SiC) is a wide semiconductor material with superior material properties compared to other rival materials. Due to its fewer dislocation defects than gallium nitride and its ability to form native oxides, this material possesses an advantage among wide band gap materials. Despite having several superior properties its low voltage application is less explored. CMOS is extremely important in low voltage areas and silicon is the dominant player in it for the last 50 years where scaling has contributed a major role in this flourishment. The channel length of silicon devices has reached 3 nm whereas SiC is still in the micrometer (2 μm/ 1.2 μm) range. So, SiC technology is still in its infancy which can be compared with silicon technology in the mid-1980s range. When the SiC devices would enter into the sub-micron and deep submicron range, proper device design in those ranges is necessary to rip the benefit of scaling. In this thesis, the SiC CMOS process available from different institutes and foundries is discussed first to understand the current state of the art. Later, low-voltage conventional SiC NMOS devices in the submicron range (2 μm to 600 nm) are simulated and their key parameters and performances are analyzed. In the submicron range, one major issue in MOSFET scaling is hot carrier effects. Thus to minimize this effect, a low-doped drain (LDD) region is introduced in the conventional SiC design having a channel length of 800 nm and 600 nm. In comparison with conventional designs, LDD designs have shown better saturation current behavior, reduced threshold roll-off, reduced hot electron current density, minimized gate leakage, reduced body hole current, enhanced voltage handling capability, reduced electric field, and improved subthreshold behavior in SiC. In the end, spacer technology, dopants, doping methods, and LDD realization technique in SiC are discussed

Advanced CMOS Process for Submicron Silicon Carbide (SiC) Device

Silicon carbide (SiC) is a wide semiconductor material with superior material properties compared to other rival materials. Due to its fewer dislocation defects than gallium nitride and its ability to form native oxides, this material possesses an advantage among wide band gap materials. Despite having several superior properties its low voltage application is less explored. CMOS is extremely important in low voltage areas and silicon is the dominant player in it for the last 50 years where scaling has contributed a major role in this flourishment. The channel length of silicon devices has reached 3 nm whereas SiC is still in the micrometer (2 μm/ 1.2 μm) range. So, SiC technology is still in its infancy which can be compared with silicon technology in the mid-1980s range. When the SiC devices would enter into the sub-micron and deep submicron range, proper device design in those ranges is necessary to rip the benefit of scaling. In this thesis, the SiC CMOS process available from different institutes and foundries is discussed first to understand the current state of the art. Later, low-voltage conventional SiC NMOS devices in the submicron range (2 μm to 600 nm) are simulated and their key parameters and performances are analyzed. In the submicron range, one major issue in MOSFET scaling is hot carrier effects. Thus to minimize this effect, a low-doped drain (LDD) region is introduced in the conventional SiC design having a channel length of 800 nm and 600 nm. In comparison with conventional designs, LDD designs have shown better saturation current behavior, reduced threshold roll-off, reduced hot electron current density, minimized gate leakage, reduced body hole current, enhanced voltage handling capability, reduced electric field, and improved subthreshold behavior in SiC. In the end, spacer technology, dopants, doping methods, and LDD realization technique in SiC are discussed

Studies on Phytochemical Analysis, Antioxidant, Antibacterial and Larvicidal Properties of the Acacia nilotica Fruit Extracts

In the present study fruit of Acacia nilotica was chosen to evaluate its antioxidant, larvicidal and antibacterial properties. Phytochemical screening of aqueous and alcohol extracts of the plant fruits affirmed the presence of carbohydrates, reducing sugar, phenol, flavonoid, terpenoid, saponin and steroid. The antioxidant properties of the aqueous and alcohol fruit extracts were noted as 10.11±0.035 and 9.75±0.023 mM of FeSO4 for Ferric Reducing Antioxidant Power (FRAP) assay respectively. The potency of antimicrobial activity of the aqueous and alcohol extract of A. nilotica fruit were tested against Pseudomonas aeruginosa, Staphylococcus aureus, Bacillussubtilis, and Escherichiacoli. The alcohol extract against E. coli, exhibited the highest antimicrobial activity. Aqueous extracts of A. nilotica fruit showed potential toxicity against Aedes albopictus larvae with LC50 value of 142.074 mg L-1. Present findings clearly indicated that A. nilotica fruit extracts could be most effectively used as a natural antioxidant, antibacterial and larvicidal agent

MonArch: Network Slice Monitoring Architecture for Cloud Native 5G Deployments

Automated decision making algorithms are expected to play a key role in management and orchestration of network slices in 5G and beyond networks. State-of-the-art algorithms for automated orchestration and management tend to rely on data-driven methods which require a timely and accurate view of the network. Accurately monitoring an end-to-end (E2E) network slice requires a scalable monitoring architecture that facilitates collection and correlation of data from various network segments comprising the slice. The state-of-the-art on 5G monitoring mostly focuses on scalability, falling short in providing explicit support for network slicing and computing network slice key performance indicators (KPIs). To fill this gap, in this paper, we present MonArch, a scalable monitoring architecture for 5G, which focuses on network slice monitoring, slice KPI computation, and an application programming interface (API) for specifying slice monitoring requests. We validate the proposed architecture by implementing MonArch on a 5G testbed, and demonstrate its capability to compute a network slice KPI (e.g., slice throughput). Our evaluations show that MonArch does not significantly increase data ingestion time when scaling the number of slices and that a 5-second monitoring interval offers a good balance between monitoring overhead and accuracy.Comment: Accepted at IEEE/IFIP NOMS 202

Convective Flow Optimization inside a Lid-Driven Chamber with a Rotating Porous Cylinder Using Darcy-Brinkman-Forchheimer Model

The active flow optimization and the entropy generation of a spinning porous cylinder on laminar mixed convective flow in a lid-driven differentially heated square chamber have been explored numerically in this study. The cold top surface of the chamber is sliding in the right direction at a fixed velocity, while the cylinder is rotating at a fixed angular velocity, either assisting or opposing the main flow. Navier–Stokes and thermal energy equations define the transport phenomena, while an averaging approach via the Darcy–Brinkman–Forchheimer model is implemented for the porous medium. Three different mixed convection cases based on Reynolds number (31.62 ≤ Re ≤ 316.23), Grashof number (103 ≤ Gr ≤ 105), and Richardson number (0.1 ≤ Ri ≤ 10) are considered in the flow optimization along with the alteration of rotational Reynolds number (Rec = 10, 0, − 10), size (λ = 0.3, 04, 0.5), and position (1–5) of the cylinder. Quantitative evaluations of thermal performance are done in terms of mean Nusselt number, Bejan number, performance evaluation criterion, and thermal performance criterion. The optimization study primarily supports clockwise rotation at the central position of the porous cylinder with specific sizes (diameters) based on the ranges of governing parameters in each simulation case. It is found that the porous cylinder’s rotation primarily determines fluid flow across the porous area

Estimation of the Capacity of a Basic Freeway and Weaving Segment Under Traditional, Autonomous, and Connected Autonomous Vehicles, Using Oversaturated Traffic Condition Data

Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) will be the standard in transportation in the future. The use of such vehicles could minimize traffic oscillation and travel time and boost safety and mobility on freeways. An AV is a self-driving vehicle that can make decisions by itself in any situation. CAVs include all the characteristics of AVs and additional communication with other vehicles or the infrastructure (signal system). The use of AVs and CAVs will substantially increase motorway capacity in upcoming decades. Moreover, vehicle dynamics will change as technology and algorithms become more commonplace. In the short term, capacity may have a negative impact on talent; however, as the algorithms become more aggressive, the results will improve. Highway Capacity Manual (HCM) may need to be updated if freeway system capacity changes. As a result, the manual should focus on enhancing two freeway segments: the fundamental freeway portion and the weaving part (case study on U.S. 101 in Los Angeles, California). A microsimulation program developed by the Planung Transport Verkehr (PTV) in Karlsruhe, Germany, was used to calibrate and evaluate Wiedemann's behavioral car-following model (CFM). The Coexist project from Europe created three types of autonomous cars: AV-cautious, AV-normal, and AV all-knowing. CFMs are vital because they measure the distance between vehicles. This is crucial for capacity. The capacity of AV cautious vehicles is decreased at all levels and penetrations. When AV-cautious autonomy evolves into AV all-knowing autonomy, the capacity of the weaving section and the BFS may rise by 33% and 36%, respectively. This study provides a method for evaluating the capacity of freeways, which we estimate using AV levels and penetrations. Transportation planners and traffic engineers may utilize these capabilities to design better traffic planning and traffic-management technology in the future. For example, highway capacity will be restricted if the AV mix is largely AV-cautious. However, the solution is likely not to expand capacity but to find ways to manage traffic as new technology develops and moves to CAVs. This research aids in the planning and design of how to bring AVs and CAVs to market

The Mothers of the Kite Fliers and Kite Runners: An Against-the-Grain Study of Khaled Hosseini’s The Kite Runner

The cover page of the novel The Kite Runner by Khaled Hosseini showcases the brilliant reviews the book received from Daily Telegraph, The Times, Sunday Express and Isabel Allende. Set on the backdrop of a politically troubled Afghanistan, the novel is a tale of friendship, love, loss, betrayal, and hope, coupled with the central Christian theme of guilt and redemption. However a closer reading of The Kite Runner reveals a bizarre reality. In this “… devastating, masterful and painfully honest story of a life crippled by an act of childhood and cowardice and cruelty” (Daily Telegraph), all we get to know are the fortunes and the miseries of the Afghan males, but what about the women? The lack of women characters is so stark that to any sentient reader, it almost appears as if the Afghan community is formed solely out of male members. Only when the story shifts out of Afghanistan, the woman becomes somewhat visible in the character of Soraya. Even then, does the woman in the house attain the same subjectivity as that of the man? Does she succeed in being relevant in the society where a child is more its father’s than its mother’s? Where do the mothers of the kite fliers and kite runners stand in a society whose spirit is best reflected in the kite fighting tournament, a passion passed from fathers to sons? This article is thus an attempt to unearth and explore the unvoiced trivialising of women as the Other in Khaled Hosseini’s The Kite Runner

Estimating the Impacts of AV and CAV and Technologies Transportation Systems for Medium, Long, and Buildout Transportation Planning Horizons

Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) are expected to have a significant impact on highways, but their planning horizon impacts have not been fully studied in the literature. This study seeks to address this gap by investigating the impact of AVs/CAVs at different stages of adoption on long-range transportation planning horizons in the United States. Planners use travel demand forecasts to make important and expensive transportation supply investment decisions, and this study uses oversaturated traffic data from the NGSIM database to estimate the parameters of the Wiedemann car-following model for a basic freeway. Using data from the European-funded Coexist Project, we construct AV/CAV scenarios that incorporate various mixes of AV/CAV technologies, including cautious driving behavior (AV-Cautious) and more aggressive driving behavior (AV All-Knowing), and span multiple planning horizon planning years. Our findings suggest that the capacity impact of AVs will change based on their penetration in the vehicle fleet. For medium-term planning horizons, AVs will reduce capacities, whereas for long-term planning horizons and the buildout, capacities will be positively impacted. However, the impact of AVs/CAVs on highway capacity is subject to two main limitations, including the assumptions made in this study about the behavior of AVs/CAVs and the lack of consideration for AVs/CAVs in oversaturated traffic in previous literature. Future studies could explore these limitations in more detail and consider other factors, such as the impact of AVs/CAVs on travel demand and the potential for AVs/CAVs to affect mode share. Overall, this research provides valuable information for transportation planners and decision-makers to consider as they develop medium and long-term transportation plans and make informed decisions about the impact of AVs/CAVs