Design of an Ultra-Compact and Highly-Sensitive Temperature Sensor Using Photonic Crystal Based Single Micro-Ring Resonator and Cascaded Micro-Ring Resonator

Silicon - In the present report, a photonic crystal based micro-ring resonator (MRR) structure is proposed which is very compact in size and has very fast response and is employed for temperature...     

Extensive Study of Underlap Length Effect for 3-D SOI FinFET to Achieve High Switching Ratio and Low Power

Silicon - The primary purpose of this work is to study the effect of symmetric and asymmetric variation of underlap regions both on source and drain side of 3D SOI n-FinFET. Underlap length is...     

Simulation Study of Dielectric Modulated Dual Material Gate TFET Based Biosensor by Considering Ambipolar Conduction

Silicon - In this paper, a dielectric modulated dual material gate TFET (DM-DMG_TFET)based biosensor is proposed. In order to detect various biomolecules, a nanogap cavity is formed by the...     

Design of Dual-Gate P-type IMOS Based Industrial Purpose Pressure Sensor

Silicon - In this treatise, we have proposed a Single Material Gate–Dual Gate Impact Ionization Metal Oxide Semiconductor (SMG DG-IMOS) based Pressure Sensor. The pressure sensor has the most...     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.

     

The design,analysis, and cost estimation of a generic adder and subtractor using the layered T (LT) logic reduction methodology with a quantum-dot cellular-automata-based approach

The quantum-dot cellular automata (QCA) is considered to be one of the ground-breaking nanotechnologies developed over the last two decades. A layered T (LT) logic cell library is constructed herein, and the methodology is extended to generic adder and subtractor module designs. The two proposed algorithms lead to more efficient QCA layout designs for an n-bit ripple carry adder (RCA) and subtractor based on an effective clock zone assignment approach. The suggested one-, four-, and eight-bit RCAs and subtractors surpass most of their existing counterparts by offering lower effective area and cell complexity. A comparative analysis is presented regarding the complexity, irreversible power dissipation, and Cost_α of the proposed n-bit layouts from a cost estimation purview.

     

A Novel High Nitrogen Steel Powder Designed for Minimized Cr2N Precipitations

High nitrogen steels provide excellent mechanical properties and corrosion resistance but are prone to form precipitates which adversely affect the corrosion resistance and toughness. High nitrogen steel powders currently available in the market are not claimed to be precipitate free. It is critical to avoid these precipitates while retaining nitrogen in the dissolved form to realize the value of these powder alloys. However, retaining high level of dissolved nitrogen in steel powder during melt atomization process is very challenging. Instead, solid-state dissolution of nitrogen into the powder alloy followed by rapid cooling may provide a convenient approach to avoid precipitate formation compared to traditional melt processing. This study presents a solution treatment approach to achieve elevated dissolved nitrogen levels (~ 0.4 wt pct) in Fe–Mn–Cr powder alloy with negligible precipitation of nitrides. The influence of starting material, holding time, temperature and cooling rate on the resulting microstructure is presented. A fully austenite matrix with high dissolved nitrogen content resulted in powders with desired mechanical properties.

     

Abrasion Resistance of Field-Assisted Ion-Exchanged Glass

The fracture strength of HF-etched samples was measured as a function of field-assisted K-Na ion exchange depth, as well as the severity of abrasion. As expected, the more severe the abrasion, the greater the depth required to make the strength insensitive to abrasion. An abrasion-independent weakening mechanism that increased with exchange depth was also found to be operative.     

Design of novel compact anti-stiction and low insertion loss RF MEMS switch

A novel torsional RF MEMS capacitive switch design on silicon substrate is presented. The optimized switch topology such as reduction in up-state capacitance results in insertion loss better than ?0.1 dB till 20 GHz. Off to on state capacitance ratio is also improved by 18 fold and isolation is better than ?43 dB at 9.5 GHz. The achieved on state return loss is ?38 dB as compared to ?21 dB at 9.5 GHz. An optimized reduction in contact area and use of floating metal layer increases the switching speed from 56 to 46 μsec. It also increases the switch reliability by alleviating the stiction.     

Analysis and optimization of fixture under dynamic machining condition with chip removal effect

Dynamic interactions in the tool–workpiece and workpiece–fixture systems significantly impinge on the quality of finished workpieces. The presented simulation system integrates the effects of workpiece fixture dynamics with the other factors contributing to the machining process dynamics. It provides more accurate prediction of the process output which helps in the design of the optimum fixture configuration prior to the production stage. Modelling of the frictional contact behaviour between the fixture element and the workpiece helps to improve the prediction accuracy of the simulation system which accelerates the convergence to the optimum fixture configuration design and consequently improves the machined part dimensional accuracy and geometric integrity. The developed simulation is capable of modelling complicated part geometries by interfacing with commercial ANSYS.V10^® packages. This research work minimizes the deformation of workpiece using integrated optimization tool of Genetic algorithm (GA) and ANSYS Parametric Design Language (APDL) of finite element analysis. The same layouts given by the above optimization tool are used in the experimental setup and it is found that the improved geometric tolerance of squarness and flatness of the given workpiece. The chip removal effect and frictional contact between the workpiece and the fixture elements are taken into account based on element death technique and nonlinear finite-element analysis. A Case study of an open slot milling process illustrates the application of the proposed improved geometric tolerance approach.