Minimizing power dissipation in optical interconnects at low voltage using optimal modulator design

A strong motivation for insertion of optical interconnects in short-distance applications such as chip-to-chip or back-plane communication, apart from high bit rates, is their potential to achieve these bit rates at low power compared to the currently prevalent copper based interconnects. Thus, it is imperative to construct design methodologies which minimize the total optical link power dissipation. We present one such methodology, where we optimize the quantum-well modulators to minimize the power dissipation in modulator-based optical interconnects. In the first part of the paper, the focus is on obtaining the optimal modulator metrics [contrast ration (CR) and insertion loss], which yield the lowest total power (receiver and the modulator). The trends are studied as a function of the input laser power and bit rate. Having obtained the desirable modulator metrics and the corresponding power dissipation, in the second part, the focus is on the feasibility of these metrics in the light of voltage swing constraints. The biggest concern with the modulator based optical link is the low CR, especially at low voltage swing. While studying these concerns, we also provide insight into the physical design of the modulator including, its intrinsic region thickness, pre-bias voltage, and the size and the number of quantum-wells. Specifically, we outline the method to obtain the design parameters, which allows minimum power dissipation with the least laser power. This ultimately yields higher aggregate I/O bandwidth for chip to chip communication in power limited chips.     

A fast algorithm and practical considerations for passive macromodeling of measured/simulated data

Passive macromodeling of high-speed package and interconnect modules characterized by measured/simulated data has generated immense interest during the recent years. This paper presents an efficient algorithm for transient simulation of interconnect networks characterized by measured/simulated data in the presence of other linear and nonlinear devices. A new set of linear constraints are proposed, which help in preserving the passivity of resulting macromodels. Examples are presented to demonstrate the validity and efficiency of the proposed algorithm.     

Structural and electrical measurements of CdZnSe composite

TheI—Vcharacterization and the electrical resistivity of selenium rich Se₈₅Cd_15-xZn_x (x = 0, 3, 7, 11 and 15) system at room temperature have been studied. Samples were obtained using melt cooling technique. So prepared samples were then characterized in terms of their crystal structure and lattice parameter using X-ray diffraction method. The materials were found to be poly crystalline in nature, having zinc blend structure over the whole range of zinc concentration. The measurements ofI—V bdcharacteristics have been carried out at different temperatures from room to 140°C. The electrical resistivity of the samples with composition at room temperature has been found to vary between maximum 2.7 x 10⁸ Ωm and minimum 7.3 x 10⁵ Ωm and shows a maximum at 3 at. wt.% of Zn. The carrier activation energy of the samples with composition has also been determined and found to vary from 0.026 eV to 0.111 eV.     

SiO2 degradation with charge injection polarity

We have investigated gate oxide degradation in metal-oxide-semiconductor (MOS) devices as a function of high-field constant-current stress for charge injection from both gate and substrate. The two polarities are asymmetric: gate injection, where the substrate Si-SiO₂ interface is the collecting electrode for the energetic electrons, shows a higher rate of interface-state generation (ΔD_it) and lower charge-to-breakdown Q_bd. Thus the collecting electrode interface, which suffers primary damage, emerges as a critical degradation site in addition to the injecting electrode interface, which has been the traditional focus. Consistent with a physical-damage model of breakdown, we demonstrate that interfacial degradation is an important precursor of breakdown, and that the nature of breakdown-related damage is physical, such as trap-generation by broken bonds     

Thermal studies of Cr2O3 doped CaCO3-SiO2 (2∶1) system

Mixtures of CaCO₃SiO₂ in 21 molar ratio were subjected separated to thermal analysis with varying concentration of Cr₂O₃ (0.1 to 5%) as dopant. The activation energy (E _a) and enthalpy (H) shows a decreasing trend with 0.1 to 1% Cr₂O₃ and attains a minimum value with 1% dopant. 0.1 to 0.5% Cr₂O₃ helps in the formation of and C₂S, (Cement Chemistry notations, C = CaO, S = SiO₂) phases at 1400° C and above but 1% Cr₂O₃ stabilizes -C₂S phase along with a little free lime and CaCrO₄. A small quantity of CaCrO₄, Cr₂SiO₂ and -C₂S are also formed along with the major phases with 5% Cr₂O₃ indicating that Cr³⁺ can substitute both Ca²⁺ and Si⁴⁺ ions in the C₂S lattice.     

Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment.     

Mesoporous titanium dioxide nanocatalyst: a recyclable approach for one‐pot synthesis of 5‐hydroxymethylfurfural

The present study deals with the production of 5‐hydroxymethyl furfural (HMF) from fructose by chemo‐conversion method using chemical catalyst, conventionally achieved by microwave‐assisted dehydration process. Five different chemical catalysts, namely oxalic acid, phosphotungstic acid and mesoporous titanium dioxide nanoparticles (TNPs) were compared at constant conditions of which TNPs yielded a maxima of 33.95%. The optimum temperature and catalyst loading were found to be 200°C and 20%, respectively, at a 5% optimum substrate concentration during 15 min optimum reaction time to yield 61.53% HMF. The efficiency of synthesised TNPs was investigated further through reusability studies. TNPs were properly recycled and the catalytic activity recovery was good even after a 14 batch reactions. The specific surface area of the TNP obtained is about 105.46 m² /g and its pore‐volume is about 0.42 cm³ /g according to single point adsorption. A large accessible surface area combined with a minimal pore size (15.92 nm) obtained with mesoporous TNPs is desirable for better catalyst loading, high‐yield HMF, retention and reduced diffusion constraints.Inspec keywords: mesoporous materials, recycling, production management, dissociation, nanoparticles, nanotechnologyOther keywords: mesoporous titanium dioxide nanocatalyst, recyclable approach, one‐pot synthesis, 5‐hydroxymethyl furfural production, HMF, chemo‐conversion method, chemical catalyst, microwave‐assisted dehydration process, oxalic acid, phosphotungstic acid, mesoporous titanium dioxide nanoparticles, TNP     

Classification of patterns representing Apples and Oranges in three-qubit system

The study of the classification of Apples and Oranges in a warehouse has been undertaken in a three-qubit system using the method of repeated iterations in Grover’s algorithm and Ventura’s algorithm separately. Operator describing an inversion about average has been constructed as a square matrix of order eight, the phase inversion operators and corresponding iteration operators for patterns separately representing Apples and Oranges have been derived, and various possible superpositions as the choice for search states for the classification of these patterns have been obtained for starting states consisting of two patterns and a single pattern, respectively. It has been demonstrated that on the second iteration of the exclusion superposition by the corresponding iteration operators, the patterns Apples and Oranges, respectively, are most suitably classified using the Grover’s algorithm. The probabilities of classifications of Apples have also been calculated by using Ventura’s algorithm (Ventura and Martinez in Inf Sci 124:273–296, 2000; Found Phys Lett 12:547–559, 1999) for all the possible superpositions as the search states, and the results have been compared with those of Grover’s algorithm and it has been demonstrated that in general for classification of a given pattern (Apples) in three-qubit system, the Grover’s and Ventura’s algorithms are effective in the cases where the number of patterns in the stored database is larger or smaller, respectively.     

DFuzzy: a deep learning-based fuzzy clustering model for large graphs

Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.     

Poly(acrylamide) functionalized chitosan: An efficient adsorbent for azo dyes from aqueous solutions

In the present communication we report on the optimization of persulfate/ascorbic acid initiated synthesis of chitosan-graft-poly(acrylamide) (Ch-g-PAM) and its application in the removal of azo dyes. The optimum yield of the copolymer was obtained using 16 × 10⁻² M acrylamide, 3.0 × 10⁻² M ascorbic acid, 2.4 × 10⁻³ M K₂S₂O₈ and 0.1 g chitosan in 25 mL of 5% aqueous formic acid at 45 ± 0.2 °C. Ch-g-PAM remained water insoluble even under highly acidic conditions and could efficiently remove Remazol violet and Procion yellow dyes from the aqueous solutions over a pH range of 3–8 in contrast to chitosan (Ch) which showed pH dependent adsorption. The adsorption data of the Ch-g-PAM and Ch for both the dyes were modeled by Langmuir and Freundlich isotherms where the data fitted better to Langmuir isotherms. To understand the adsorption behavior of Ch-g-PAM, adsorption of Remazol violet on to the copolymer was optimized and the kinetic and thermodynamic studies were carried out taking Ch as reference. Both Ch-g-PAM and Ch followed pseudo-second-order adsorption kinetics. The thermodynamic study revealed a positive heat of adsorption (ΔH°), a positive ΔS° and a negative ΔG°, indicating spontaneous and endothermic nature of the adsorption of RV dye on to the Ch-g-PAM. The Ch-g-PAM was found to be very efficient in removing color from real industrial wastewater as well, though the interfering ions present in the wastewater slightly hindered its adsorption capacity. The data from regeneration efficiencies for ten cycles evidenced the high reusability of the copolymer in the treatment of waste water laden with even high concentrations of dye.