Analysis of read speed latency in 6T-SRAM cell using multi-layered graphene nanoribbon and cu based nano-interconnects for high performance memory circuit design

In this study, we designed a 6T-SRAM cell using 16-nm CMOS process and analyzed the performance in terms of read-speed latency. The temperature-dependent Cu and multilayered graphene nanoribbon (MLGNR)-based nano-interconnect materials is used throughout the circuit (primarily bit/bit-bars [red lines] and word lines [write lines]). Here, the read speed analysis is performed with four different chip operating temperatures (150K, 250K, 350K, and 450K) using both Cu and graphene nanoribbon (GNR) nano-interconnects with different interconnect lengths (from 10 μm to 100 μm), for reading-0 and reading-1 operations. To execute the reading operation, the CMOS technology, that is, the16-nm PTM-HPC model, and the16-nm interconnect technology, that is, ITRS-13, are used in this application. The complete design is simulated using TSPICE simulation tools (by Mentor Graphics). The read speed latency increases rapidly as interconnect length increases for both Cu and GNR interconnects. However, the Cu interconnect has three to six times more latency than the GNR. In addition, we observe that the reading speed latency for the GNR interconnect is ~10.29 ns for wide temperature variations (150K to 450K), whereas the reading speed latency for the Cu interconnect varies between ~32 ns and 65 ns for the same temperature ranges. The above analysis is useful for the design of next generation, high-speed memories using different nano-interconnect materials.     

Spectroscopic investigations on Dy3+ ions doped zinc lead alumino borate glasses for photonic device applications

This paper presents the structural, optical absorption, photoluminescence (PL) and decay spectral properties of Dy~(3+)ions doped zinc lead alumino borate (ZPAB) glasses to elucidate their possible usage in photonic devices such as w-LEDs and lasers. A broad hump shown by the XRD spectrum recorded for an un-doped ZPAB glass confirms its non-crystalline nature. The Judd-Ofelt (J-O) intensity parameter evaluated from the measured oscillator strengths of the absorption spectral features were used to estimate various radiative parameters and also to understand the nature of bonding between Dy~(3+)ions and oxygen ligands. Under 350 nm excitation, the as-prepared glasses are exhibiting two emission bands~4F_(9/2)→~6H_(15/2)(blue),and~4F_(9/2)→~6H_(13/2)(yellow) at 483 and 575 nm,respectively. From the PL spectra,the Y/B ratio values, CIE chromaticity color coordinates and color correlated temperature (CCT) were evaluated. The experimental lifetimes measured from the decay profiles are decreasing with increase in Dy~(3+)ions concentration in these glasses which may be attributed to the cross-relaxation and nonradiative multiphonon relaxation process. Decay profiles observed for higher concentration were well fitted to Inokuti-Hirayama (I-H) model to understand the energy transfer process and subsequent decrease in experimental lifetimes. The higher values of radiative parameters, emission cross-sections,quantum efficiency, optical gain and gain band width suggest the suitability of 0.5 mol%of Dy~(3+) ions in these ZPAB glasses for the photonic device application.     

Gate-stack optimization of a vertically stacked nanosheet FET for digital/analog/RF applications

Journal of Computational Electronics - Nanosheet field effect transistors (NS-FET) are a most promising candidate for next-generation semiconductor devices for sub-7-nm technology nodes. This work...     

Image segmentation using multilevel thresholding based on type II fuzzy entropy and marine predators algorithm

Multimedia Tools and Applications - The digital image segmentation is an open problem that is growing day by day and is attracting the attention of researchers from last few years. Image resolution...     

An analysis of the eddy effect in through-silicon vias based on Cu and CNT bundles: the impact on crosstalk and power

Journal of Computational Electronics - The performance of three-dimensional integrated circuits primarily depends on the filler material used in the through-silicon vias (TSVs). The most widely...     

Cryogenic minimum quantity lubrication machining: from mechanism to application

Cutting fluid plays a cooling–lubrication role in the cutting of metal materials. However, the substantial usage of cutting fluid in traditional flood machining seriously pollutes the environment and threatens the health of workers. Environmental machining technologies, such as dry cutting, minimum quantity lubrication (MQL), and cryogenic cooling technology, have been used as substitute for flood machining. However, the insufficient cooling capacity of MQL with normal-temperature compressed gas and the lack of lubricating performance of cryogenic cooling technology limit their industrial application. The technical bottleneck of mechanical–thermal damage of difficult-to-cut materials in aerospace and other fields can be solved by combining cryogenic medium and MQL. The latest progress of cryogenic minimum quantity lubrication (CMQL) technology is reviewed in this paper, and the key scientific issues in the research achievements of CMQL are clarified. First, the application forms and process characteristics of CMQL devices in turning, milling, and grinding are systematically summarized from traditional settings to innovative design. Second, the cooling–lubrication mechanism of CMQL and its influence mechanism on material hardness, cutting force, tool wear, and workpiece surface quality in cutting are extensively revealed. The effects of CMQL are systematically analyzed based on its mechanism and application form. Results show that the application effect of CMQL is better than that of cryogenic technology or MQL alone. Finally, the prospect, which provides basis and support for engineering application and development of CMQL technology, is introduced considering the limitations of CMQL.     

Controlling the Placement of Spherical Nanoparticles in Electrically Driven Polymer Jets and its Application to Li‐Ion Battery Anodes

Employing circumferentially uniform air flow through the sheath layer of the concentric coaxial nozzle, the gas‐assisted electrospinning (GAES) utilizes both high electric field and controlled air flow to produce nanofibers. The ability to tailor the distribution of various nanofillers (1.85–12.92 vol% of spherical SiO₂ and Si nanoparticles) in a polyvinyl alcohol jet is demonstrated by varying airflow rates in GAES. The distribution of nanofillers is measured from transmission electron microscopy and is analyzed using an image processing technique to perform the dispersion area analysis and obtain the most probable separation between nanoparticles using fast Fourier transform (FFT). The analysis in this study indicates an additional 350% improvement in dispersion area with the application of high but controlled airflow, and a 75 percent decrease in separation between nanoparticles from the FFT. The experiments in this study are in good agreement with a coarse‐grained MD simulation prediction for a polymer nanocomposite system subjected to extensional deformation. Lastly, utilizing the sheath layer air flow in production of Li‐battery anode material, a 680 mAh g^?1 improvement is observed in capacity for nanofibers spun via GAES compared to ES at the same Si NP loading, which is associated with better dispersion of the electrochemically active nanoparticles.     

An intelligent recommendation system using gaze and emotion detection

Recently, recommendation system has become popular in many e-commerce websites. It helps users by suggesting products which they could buy. Existing work till now uses past feedback of user, similarity of other users’ buying pattern, or a hybrid approach in which both type of information is used. But the pitfall of these approaches is that there is a need to collect and process huge amount of data for good recommendation. This paper is aimed at developing an efficient recommendation system by incorporating user’s emotion and interest to provide good recommendations. The proposed system does not require any of aforementioned data and works without the continuous and interminable attention of the user. In this framework, we capture user’s eye-gaze and facial expression while exploring websites through inexpensive, visible light “webcam”. The eye-gaze detection method uses pupil-center extraction of both eyes and calculates the reference point through a joint probability. The facial expression uses landmark points of face and analyzes the emotion of the user. Both methods work in approximate real time and the proposed framework thus provides intelligent recommendations on-the-fly without requirement of feedback and buying patterns of users.

     

Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self-Guided Ants (ASGA)

Decrease in product life along with the advent of stringent regulations and environmental consciousness have led to increased concern for methodological product recovery through disassembly operations. This research proposes a fuzzy disassembly optimization model (FDOM) and is aimed at determining the optimal disassembly sequence as well as the optimal depth of disassembly to maximize the net revenue at the end-of-life (EOL) disposal of the product in the real world situations. In order to account for the uncertainty inherent in quality of the returned products, fuzzy control theory is incorporated in the problem environment for modeling the expected value of the recovered modules. Considering the computational complexity of the problem at hand, an innovative approach of Algorithm of Self-Guided Ants (ASGAs) is proposed for the same. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment techniques and analysis of variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization and Genetic Algorithm over which it always demonstrated better results thereby proving its superiority on the concerned problem.     

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find unknown nodes in Three-Dimensional environment, only single anchor node is used. In the simulation-based environment, the nodes with unknown locations are moving at middle & lower layers whereas the top layer is equipped with single anchor node. A novel soft computing technique namely Adaptive Plant Propagation Algorithm (APPA) is introduced to obtain the optimized locations of these mobile nodes. These mobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity (Degree of Irregularity (DOI)) value set to 0.01. The simulation results present that proposed APPA algorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error, computational time, and the located sensor nodes.