Self-aligning planarization and passivation for integration applications in III-V semiconductor devices

This work reports an easy planarization and passivation approach for the integration of III-V semiconductor devices. Vertically etched III-V semiconductor devices typically require sidewall passivation to suppress leakage currents and planarization of the passivation material for metal interconnection and device integration. It is, however, challenging to planarize all devices at once. This technique offers wafer-scale passivation and planarization that is automatically leveled to the device top in the 1-3-/spl mu/m vicinity surrounding each device. In this method, a dielectric hard mask is used to define the device area. An undercut structure is intentionally created below the hard mask, which is retained during the subsequent polymer spinning and anisotropic polymer etch back. The spin-on polymer that fills in the undercut seals the sidewalls for all the devices across the wafer. After the polymer etch back, the dielectric mask is removed leaving the polymer surrounding each device level with its device top to atomic scale flatness. This integration method is robust and is insensitive to spin-on polymer thickness, polymer etch nonuniformity, and device height difference. It prevents the polymer under the hard mask from etch-induced damage and creates a polymer-free device surface for metallization upon removal of the dielectric mask. We applied this integration technique in fabricating an InP-based photonic switch that consists of a mesa photodiode and a quantum-well waveguide modulator using benzocyclobutene (BCB) polymer. We demonstrated functional integrated photonic switches with high process yield of >90%, high breakdown voltage of >25 V, and low ohmic contact resistance of /spl sim/10 /spl Omega/. To the best of our knowledge, such an integration of a surface-normal photodiode and a lumped electroabsorption modulator with the use of BCB is the first to be implemented on a single substrate.     

Revealing determinants of two‐phase dynamics of P53 network under gamma irradiation based on a reduced 2D relaxation oscillator model

This study proposes a two‐dimensional (2D) oscillator model of p53 network, which is derived via reducing the multidimensional two‐phase dynamics model into a model of ataxia telangiectasia mutated (ATM) and Wip1 variables, and studies the impact of p53‐regulators on cell fate decision. First, the authors identify a 6D core oscillator module, then reduce this module into a 2D oscillator model while preserving the qualitative behaviours. The introduced 2D model is shown to be an excitable relaxation oscillator. This oscillator provides a mechanism that leads diverse modes underpinning cell fate, each corresponding to a cell state. To investigate the effects of p53 inhibitors and the intrinsic time delay of Wip1 on the characteristics of oscillations, they introduce also a delay differential equation version of the 2D oscillator. They observe that the suppression of p53 inhibitors decreases the amplitudes of p53 oscillation, though the suppression increases the sustained level of p53. They identify Wip1 and P53DINP1 as possible targets for cancer therapies considering their impact on the oscillator, supported by biological findings. They model some mutations as critical changes of the phase space characteristics. Possible cancer therapeutic strategies are then proposed for preventing these mutations’ effects using the phase space approach.Inspec keywords: physiological models, cellular biophysics, cancer, difference equations, delays, enzymes, biochemistry, molecular biophysics, gamma‐rays, radiation therapyOther keywords: two‐phase dynamics model, P53 network, gamma irradiation, 2D relaxation oscillator model, ATM model, Wip1 variables, p53‐regulators, cell fate decision, excitable relaxation oscillator, Wip1 time delay, state‐dependent delay differential equation, cell cycle arrest, cell apoptosis, cancer therapies, Wip1 overexpression, Wip1 downregulation, ATM deficiency, Mdm2 overexpression, Mdm2 downregulation, mutation effects, phase space approach     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.     

Investigation of Ant System parameter interactions by using design of experiments for job-shop scheduling problems

In recent years, one of the most important and promising research fields has been metaheuristics to find optimal or near-optimal solutions for NP-hard combinatorial optimization problems. Improving the quality of the solution or the solution time is basic research area on metaheuristics. Modifications of the existing ones or creation of hybrid approaches are the focus of these efforts. Another area of improving the solution quality of metaheuristics is finding the optimal combination of algorithm control parameters. This is usually done by design of experiments or one-at-a-time approach in genetic algorithms, simulated annealing and similar metaheuristics. We observe that, in studies which use Ant Colonies Optimization (ACO) as an optimization technique; the levels of control parameters are determined by some non-systematic initial experiments and the interactions of the parameters are not studied yet.In this study, the parameters of Ant System have been investigated on different sized and randomly generated job-shop scheduling problems by using design of experiments. The effects and interactions of the parameters have been interpreted with the outputs of the experiments. Referring to the statistical analysis it is observed that none of the interactions between the Ant System parameters has a significant effect on makespan value. A specific fractional experimental design is suggested instead of the full factorial design. Depending on the findings from the benchmark problems it will be a reliable approach to use the suggested design for saving time and effort in experiments without sacrificing the solution quality.     

Perinatal Obesity Induces Hepatic Growth Restriction with Increased DNA Damage Response,Senescence, and Dysregulated Igf-1-Akt-Foxo1 Signaling in Male Offspring of Obese Mice

Maternal obesity predisposes for hepato-metabolic disorders early in life. However, the underlying mechanisms causing early onset dysfunction of the liver and metabolism remain elusive. Since obesity is associated with subacute chronic inflammation and accelerated aging, we test the hypothesis whether maternal obesity induces aging processes in the developing liver and determines thereby hepatic growth. To this end, maternal obesity was induced with high-fat diet (HFD) in C57BL/6N mice and male offspring were studied at the end of the lactation [postnatal day 21 (P21)]. Maternal obesity induced an obese body composition with metabolic inflammation and a marked hepatic growth restriction in the male offspring at P21. Proteomic and molecular analyses revealed three interrelated mechanisms that might account for the impaired hepatic growth pattern, indicating prematurely induced aging processes: (1) Increased DNA damage response (γH2AX), (2) significant upregulation of hepatocellular senescence markers (Cdnk1a, Cdkn2a); and (3) inhibition of hepatic insulin/insulin-like growth factor (IGF)-1-AKT-p38-FoxO1 signaling with an insufficient proliferative growth response. In conclusion, our murine data demonstrate that perinatal obesity induces an obese body composition in male offspring with hepatic growth restriction through a possible premature hepatic aging that is indicated by a pathologic sequence of inflammation, DNA damage, senescence, and signs of a possibly insufficient regenerative capacity.     

Multipath channel identification by using global optimization in ambiguity function domain

A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.     

RF-MEMS load sensors with enhanced Q-factor and sensitivity in a suspended architecture

In this paper, we present and demonstrate RF-MEMS load sensors designed and fabricated in a suspended architecture that increases their quality-factor (Q-factor), accompanied with an increased resonance frequency shift under load. The suspended architecture is obtained by removing silicon under the sensor. We compare two sensors that consist of 195 μm × 195 μm resonators, where all of the resonator features are of equal dimensions, but one’s substrate is partially removed (suspended architecture) and the other’s is not (planar architecture). The single suspended device has a resonance of 15.18 GHz with 102.06 Q-factor whereas the single planar device has the resonance at 15.01 GHz and an associated Q-factor of 93.81. For the single planar device, we measured a resonance frequency shift of 430 MHz with 3920 N of applied load, while we achieved a 780 MHz frequency shift in the single suspended device. In the planar triplet configuration (with three devices placed side by side on the same chip, with the two outmost ones serving as the receiver and the transmitter), we observed a 220 MHz frequency shift with 3920 N of applied load while we obtained a 340 MHz frequency shift in the suspended triplet device with 3920 N load applied. Thus, the single planar device exhibited a sensitivity level of 0.1097 MHz/N while the single suspended device led to an improved sensitivity of 0.1990 MHz/N. Similarly, with the planar triplet device having a sensitivity of 0.0561 MHz/N, the suspended triplet device yielded an enhanced sensitivity of 0.0867 MHz/N.     

Response Surface Modeling and Evaluation of the Influence of Deposition Parameters on the Electrolytic Cu-Sn Alloy Powders Production

In this article, the electrodeposition process of Cu-Sn alloy powders from tripolyphosphate (TPP)-based electrolytes was investigated as a function of deposition parameters. The effects of deposition parameters such as current density, electrolyte composition (Cu/Sn mole ratio), mechanical stirring speed, and temperature on the Cu content of alloy powder and cathodic current efficiency were evaluated using the response surface methodology (RSM). The empirical models developed in terms of deposition parameters were found to be statistically adequate to describe the process responses. The study revealed that as far as the copper content was concerned in the alloyed powders, all parameters selected had positive correlations. However, a high stirring speed and low current density led to a greater current efficiency. The morphology and chemical composition of the electrodeposited Cu-Sn alloy powders were investigated using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and inductively coupled plasma (ICP) analysis. An SEM analysis showed that the powder morphology was affected considerably by the cathodic current density and stirring speed.     

On-Chip-Integrated Nanowire Device Platform With Controllable Nanogap for Manipulation, Capturing, and Electrical Characterization of Nanoparticles

We propose and demonstrate nanowire (NW) device platforms on-chip integrated using electric-field-assisted self-assembly. This platform integrates from nanoprobes to microprobes, and conveniently allows for on-chip manipulation, capturing, and electrical characterization of nanoparticles (NPs). Synthesizing segmented (Au-Ag-Au) NWs and aligning them across predefined microelectrode arrays under ac electric field, we controllably form nanogaps between the self-aligned end (Au) segments by selectively removing the middle (Ag) segments. We precisely control and tune the size of this middle section for nanogap formation in the synthesis process. Using electric field across nanogaps between these nanoprobes, we capture NPs to electrically address and probe them at the nanoscale. This approach holds great promise for the construction of single NP devices with electrical nanoprobe contacts.