Influence of ion-beam sputtering deposition parameters on highly photosensitive and transparent CdZnO thin films

CdZnO thin films with a nominal thickness of ~200 nm were grown on c-plane sapphire substrates by dual ion-beam sputtering deposition technique. The effect of substrate temperature (300–600 °C) and gas ambience on structural, morphological, compositional and opto-electronic properties was studied. X-ray diffraction patterns confirmed that all the films were polycrystalline in nature and were preferentially oriented along the c-axis. It was revealed that the films grown at Ar/O₂ ratio of 4:1 were structurally more ordered and the film quality was found to be the best at 500 °C. The compositional studies specify that approximately 11.8 at.% of cadmium were present in the film deposited at 300 °C in Ar–O₂ mixture. Investigations on optical properties by photoluminescence and absorption studies indicate band gap shrinkage with the increase in argon partial pressure and substrate temperature. It was found that photosensitivity of the deposited films was highly dependent on growth conditions. The photosensitivity was found to be 5000-fold higher for CdZnO film grown at 600 °C in Ar–O₂ ambience compared to the best reported result, and this was promising to realize high-performance opto-electronic devices on such CdZnO films.     

Short Antimicrobial Lipo‐α/γ‐AA Hybrid Peptides

The last two decades have seen the rise of antimicrobial peptides (AMPs) to combat emerging antibiotic resistance. Herein we report the solid‐phase synthesis of short lipidated α/γ‐AA hybrid peptides. This family of lipo‐chimeric peptidomimetics displays potent and broad‐spectrum antimicrobial activity against a range of multi‐drug resistant Gram‐positive and Gram‐negative bacteria. These lipo‐α/γ‐AA hybrid peptides also demonstrate high biological specificity, with no hemolytic activity towards red blood cells. Fluorescence microscopy suggests that these lipo‐α/γ‐AA chimeric peptides can mimic the mode of action of AMPs and kill bacterial pathogens via membrane disintegration. As the composition of these chimeric peptides is simple, therapeutic development could be economically feasible and amenable for a variety of antimicrobial applications.     

Structural characteristics,fluorescence quenching,and antioxidant activity of the arabinogalactan protein-rich fraction from senna (<Emphasis Type="Italic">Cassia angustifolia</Emphasis>) leaves

Senna (Cassia angustifolia) is widely used in Indian folk medicine for the prevention of various disorders. Herein, we have demonstrated that water extracted arabinogalactan protein (AGP) rich fraction possesses strong antioxidative activity. The AGP rich fraction was analyzed using chemical, chromatographic, and spectroscopic methods. Effect of water extracted polymers on bovine serum albumin spectrum was determined using excitation wavelength of 282 nm. The antioxidant capacity of this fraction was studied by ferric reducing antioxidant power (FRAP) and DPPH radical assays. This polymeric fraction, ≥88% of which gets precipitated with Yariv reagent, consisted mainly of (1→5)-/(1→3,5)-linked α-arabinosyl, (1→3)-/(1→3,6)-linked-galactosyl residues and terminal arabinofuranosyl residues. Its in vitro antioxidant capacity is comparable to that of standard antioxidants. Fluorescence quenching studies furnishes evidence for the interaction of the arabinogalactan protein with bovine serum albumin.     

Mechano‐Morphological Characterization of Polyethylene‐Glycol Based Polyurethane Microgel

Segmental polyurethanes (PU) with hydrophilic segments form colloidal dispersions which are ultimately arrested into gel‐like structure in aqueous continuous phase owing to the differential interactions between polymer and solvent. These structural states of amphiphilic PUs evolve hierarchically, but the structure‐function correlation between PU colloidal dispersion and gels is not clear. Here, this correlation is defined from the mechanomorphology of hydrophilic polyethylene glycol based PU which forms dispersions and finally transforms into gel‐structure. Morphological and rheological analyses show that PU with comparable hydrophilic and hydrophobic content forms attractive colloids with self‐similar fractal microstructures whereas PU with increased hydrophilic character forms space‐filling colloids without any defined organization. Furthermore, colloidal dispersions are densified under shear or gravity to form gel where gel mechanics is defined by colloidal particle organization and the morphology is dependent on gelation mode. This stepwise organization of PU colloidal particles into microgel can independently control microgel mechanics and morphology.

     

A study on vulnerability and presentation attack detection in palmprint verification system

As biometric systems become ubiquitous in the domain of personal authentication, it is of utmost importance that these systems are secured against attacks. Among various types of attacks on biometric systems, the presentation attack, which involves presenting a fake copy (artefact) of the real biometric to the biometric sensor to gain illegitimate access, is the most common one. Despite the serious threat posed by these attacks, not much work has been done to address this vulnerability in palmprint-based biometric systems. This paper demonstrates the vulnerability of a palmprint verification system to presentation attacks and proposes a novel presentation attack detection (PAD) approach to discriminating between real biometric samples and artefacts. The proposed PAD approach is inspired by a work that established relationship between the surface reflectance and a set of statistical features extracted from the image. Specifically, statistical features computed from the distributions of pixel intensities, sub-band wavelet coefficients and the grey-level co-occurrence matrix form the original feature set, and CFS-based feature selection approach selects the most discriminating feature subset. A trained binary classifier utilizes the selected feature subset to determine whether the acquired image is of real hand or an artefact. For performance evaluation, an antispoofing database—PALMspoof has been developed. This database comprises left- and right-hand images of 104 subjects, and three kinds of artefacts generated from these images. In addition to PALMspoof database, the biometric system’s vulnerability has been assessed on display and print artefacts generated from two publicly available palmprint datasets. Our experimental results show that 1) the palmprint verification system is highly vulnerable with spoof acceptance of 84.56%; 2) the proposed PAD approach is effective against both print and display attacks, in both same-device and cross-device scenarios; and 3) the proposed approach for PAD provides an average improvement of 12.73 percentage points in classification error rate over local binary pattern (LBP)-based PAD approach.     

Artificial neural network approach for LNA design of GPS receiver

Paper presents an ANN modeling of microwave LNA for the global positioning front end receiver, operating at 1.57542 GHz. To design LNA, multilayer perceptron architecture is used. The scattering parameters of LNA are calculated using Levenberg Marquardt Backpropagation Algorithm for the frequency range 100 MHz to 8 GHz. The inputs given to this architecture are drain to source current, drain to source voltage, temperature and frequency and the outputs are maximum available gain, noise figure and scattering parameters (magnitude as well as angle). ANN model is trained using Agilent MGA 72543 GaAs pHEMT Low Noise Amplifier datasheet and this model shows high regression. The smith and polar charts are plotted for frequency range 100 MHz to 8 GHz.     

An innovative interconnect structure with improved Elmore delay estimation model for deep submicron technology

With advancements in technology, size and speed have been the important facet in VLSI interconnects. The channel length of the device reduces to tens of nanometers, as the technology is transferring to the deep submicron level. This leads to the requirement of long interconnects in VLSI chips. Interconnects are known as the basic building block that can vary from size to size. They provide a connection between two or more blocks and have scaling problems that an IC designer faces while designing. As scaling increases, the impact of interconnect in the VLSI circuits became even more important. It controls all the important electrical characteristics on the chip. With scale-down technology, interconnects not only become closer with each other but their dimensions also change which can directly impact the circuit parameters. Certain RC models have already been defined to control these parameters but in this paper, authors have proposed a new improved Elmore delay estimation model (RC) to reduce delay and power consumption in interconnect circuits. An optimized Elmore delay calculation was performed for uniform and non-uniform wires to reduce the time constant of the interconnect circuits. Further, the proposed model is estimated and verified theoretically. A new improved RC model is compared to the designed π-model that shows remarkable results. We also observed the linear relationship of power consumption and delay for both the RC models and found that in π-model, upon decreasing the length of wire the power first increases then decreases but in the proposed model, the power first increases then remain constant and then further increases upon increasing the length of wire. Our proposed model shows the remarkable values as the average percentage improvement of power is 75.167% and delay as 74.714% is achieved using a uniform distribution.

     

Light–Matter Interaction in Quantum Confined 2D Polar Metals

This work is a systematic experimental and theoretical study of the in-plane dielectric functions of 2D gallium and indium films consisting of two or three atomic metal layers confined between silicon carbide and graphene with a corresponding bonding gradient from covalent to metallic to van der Waals type. k-space resolved free electron and bound electron contributions to the optical response are identified, with the latter pointing towards the existence of thickness dependent quantum confinement phenomena. The resonance energies in the dielectric functions and the observed epsilon near-zero behavior in the near infrared to visible spectral range, are dependent on the number of atomic metal layers and properties of the metal involved. A model-based spectroscopic ellipsometry approach is used to estimate the number of atomic metal layers, providing a convenient route over expensive invasive characterization techniques. A strong thickness and metal choice dependence of the light–matter interaction makes these half van der Waals 2D polar metals attractive for quantum engineered metal films, tunable (quantum-)plasmonics and nano-photonics.     

Mathematical Insight into Moderate Inversion Gate Delay Variability for Ultradeep Submicron Digital Circuit Design

Voltage scaling has been extensively used in industry for decades to reduce power consumption. In recent years, exploring digital circuit operation in moderate inversion has created an interest among researchers due to its immense capability to provide a perfect tradeoff between high performance and low energy operation. But circuits operating in moderate inversion are susceptible to process variations and variability. To compute variability, statistical parameters such as the probability density function (PDF) and cumulative distribution function (CDF) are required. This paper presents an analytical model framework for delay calculations utilizing log skew normal distribution for ultradeep submicron technology nodes up to 22 nm. The CDF of the proposed model is utilized to calculate minimum and maximum delays with 3σ-accuracy providing better accuracy than the conventional methods. The obtained results are also compared with Monte Carlo simulations with errors lying within the acceptable range of 2%-4%.     

Neural Network Based Thermal Analysis of Ultradeep Submicron Digital Circuit Design

With a reduction in transistor dimensions to the nanoscale regime of 45 nm or less, quantum mechanical effects begin to reveal themselves and have an impact on key device performance parameters. As a result, in order to develop simulation tools that can be used for the design of nanoscale transistors in the future, new theories and modelling methodologies must be developed that properly and effectively capture the physics of quantum transport. An artificial neural network (ANN) is used in this paper to examine nanoscale CMOS circuits and predict the performance parameters of CMOS-based digital inverters for a temperature range of 300 K to 400 K. The training algorithm included three hidden layers with sizes of 20, 10, and 8, as well as a function fitting ANN with Bayesian Backpropagation Regularization. Further, simulation through HSPICE using Predictive Technology Model (PTM) nominal parameters has been done to compare with ANN (trained using an analytical model) results. The obtained results lie within the acceptable range of 1%-10%. Moreover, it has also been demonstrated that the ANN simulation provides a speed improvement of around 85 % over the HSPICE simulation, and that it can be easily integrated into software tools for designing and simulating complicated CMOS logic circuits.