Thermal Aging Effects on Cu Ball Shear Strength and Cu/Al Intermetallic Growth

Intermetallic growth and ball shear behavior of annealed Cu wire bonds on Al have been studied. The shear strength of Cu ball bonds decreased with time, and ductile fracture was the dominant failure mode from 125°C to 150°C. Al pad peel-off occurred as the aging temperature was increased above 150°C. The overall Cu/Al intermetallic thickness exhibited parabolic behavior as a function of time. A linear correlation was established between ball shear strength, metal peel-off occurrence, and intermetallic growth. The Cu/Al intermetallic growth activation energy was 0.23 eV, and the intermetallics identified in the experiment were CuAl₂ and CuAl.     

Optimal data gathering paths and energy-balance mechanisms in wireless networks

This paper studies the data gathering problem in wireless networks, where data generated at the nodes has to be collected at a single sink. We investigate the relationship between routing optimality and fair resource management. In particular, we prove that for energy-balanced data propagation, Pareto optimal routing and flow maximization are equivalent, and also prove that flow maximization is equivalent to maximizing the network lifetime. We algebraically characterize the network structures in which energy-balanced data flows are maximal. Moreover, we algebraically characterize communication links which are not used by an optimal flow. This leads to the characterization of minimal network structures supporting the maximal flows.We note that energy-balance, although implying global optimality, is a local property that can be computed efficiently and in a distributed manner. We suggest online distributed algorithms for energy-balance in different optimal network structures and numerically show their stability in particular setting. We remark that although the results obtained in this paper have a direct consequence in energy saving for wireless networks they do not limit themselves to this type of networks neither to energy as a resource. As a matter of fact, the results are much more general and can be used for any type of network and different types of resources.     

Pseudo-Parallel Datapath Structure for Power Optimal Implementation of 128-pt FFT/IFFT for WPAN

An optimal implementation of 128-Pt FFT/IFFT for low power IEEE 802.15.3a WPAN using pseudo-parallel datapath structure is presented, where the 128-Pt FFT is devolved into 8-Pt and 16-Pt FFTs and then once again by devolving the 16-Pt FFT into 4×4 and 2×8. We analyze 128-Pt FFT/IFFT architecture for various pseudo-parallel 8-Pt and 16-Pt FFTs and an optimum datapath architecture is explored. It is suggested that there exists an optimum degree of parallelism for the given algorithm. The analysis demonstrated that with a modest increase in area one can achieve significant reduction in power. The proposed architectures complete one parallel-to-parallel (i.e., when all input data are available in parallel and all output data are generated in parallel) 128-point FFT computation in less than 312.5 ns and thereby meet the standard specification. The relative merits and demerits of these architectures have been analyzed from the algorithm as well as implementation point of view. Detailed power analysis of each of the architectures with a different number of data paths at block level is described. We found that from power perspective the architecture with eight datapaths is optimum. The core power consumption with optimum case is 60.6 MW which is only less than half of the latest reported 128-point FFT design in 0.18u technology. Furthermore, a Single Event Upset (SEU) tolerant scheme for registers is also explored. The SEU tolerant scheme will not affect the performance, however, there is an increase power consumption of about 42 percent. Apart from the low power consumption, the advantages of the proposed architectures include reduced hardware complexity, regular data flow and simple counter based control.     

Construction of Time-Relaxed c-Broadcast Networks

C-broadcasting is an information dissemination task where a message, originated at any node in a network, is transmitted to all other nodes with the restriction that each node having the message can transmit it to almost c neighbors simultaneously. If the transmission time of the message is set to be one time unit, a minimal c-broadcast network (c-MBN) is a communication network in which c-broadcasting from any node can be accomplished in log _c+1 n time units, where n is the number of nodes and log _c+1 n is the fastest possible broadcast time. If networks are sparse, additional time units may be required to perform c-broadcasting. A time-relaxed c-broadcast network, denoted as (t,c)-RBN, is a network where c-broadcasting from any node can be completed in log _c+1 n+t time units. In this paper, a network compounding algorithm is proposed to construct large sparse (t,c)-RBNs by linking multiple copies of a time-relaxed network of small size using the structure of another time-relaxed network. Computational results are presented to show the effectiveness of the proposed algorithm.     

Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

Principal components analysis is an important and well-studied subject in statistics and signal processing. Several algorithms for solving this problem exist, and could be mostly grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this study, we propose an alternate approach that avoids deflation and gradient-search techniques. The proposed method is an on-line procedure based on recursively updating the eigenvector and eigenvalue matrices with every new sample such that the estimates approximately track their true values as would be calculated analytically from the current sample estimate of the data covariance matrix. The perturbation technique is theoretically shown to be applicable for recursive canonical correlation analysis, as well. The performance of this algorithm is compared with that of a structurally similar matrix perturbation-based method and also with a few other traditional methods like Sanger’s rule and APEX.

---

     

Proposal for a novel and simple WDM NRZ-DPSK system

A novel and simple non-return-to-zero differential phase shift keying (NRZ-DPSK) wavelength division multiplexing (WDM) system, which can simultaneously demultiplex and demodulate multiple wavelengths, is proposed and investigated in this paper.The phase-to-intensity demodulation principle is based on detuned filtering, which is achieved by using a single commercial array waveguide grating (AWG) in our scheme.By properly choosing appropriate AWG channels at the transmitter, the AWG at the receiver can act as both the demultiplexer and the demodulator of the DPSK signals.Simulations at 10, 20, and 40 Gbit/s show good flexibility and performance for the proposed system.     

Organic Nanoparticles: Preparation,Self‐Assembly,and Properties

The strong tendency of organic nanoparticles to rapidly self‐assemble into highly aligned superlattices at room temperature when solution‐cast from dispersions or spray‐coated directly onto various substrates is described. The nanoparticle dispersions are stable for years. The novel precipitation process used is believed to result in molecular distances and alignments in the nanoparticles that are not normally possible. Functional organic light‐emitting diodes (OLEDs)—which have the same host–dopant emissive‐material composition—with process‐tunable electroluminescence have been built with these nanoparticles, indicating the presence of novel nanostructures. For example, only changing the conditions of the precipitation process changes the OLED emission from green light to yellow.     

Ascertaining the importance of neurons to develop better brain-machine interfaces

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.     

Nanofluids: A New Class of Materials Produced from Nanoparticle Assemblies

We present evidence of a novel nanostructured fluid, a nanofluid, composed of molecular clusters of a polar organic dye and surfactant. These are not nanoparticles dispersed in a solvent; there are no solvent molecules present. These materials, which are solids under ambient conditions, are non‐reactively precipitated from a compressed CO₂ solution, resulting in a liquid‐like material, which we call a nanofluid. The precipitated dye–surfactant clusters are 1–4 nm in size. This nanofluid exhibits intense luminescent signatures, which are significantly blue‐shifted with respect to the dye powder or a solution of it. The X‐ray diffraction pattern did not show any structure in the low‐angle regime. The fluorinated surfactant is highly soluble in compressed CO₂. The polar dye does not dissolve in compressed CO₂ but is solubilized by electrostatic interactions with the surfactant head groups. We believe that the ultrafast and controlled precipitation from compressed CO₂ preserves the electrostatic coupling and promotes a structured molecular cluster. Additionally, we demonstrate the formation of organic nanoparticles using this controlled precipitation process from compressed CO₂.     

Hierarchical Self‐Assembly of Peptide‐Coated Carbon Nanotubes

Numerous applications, from molecular electronics to super‐strong composites, have been suggested for carbon nanotubes. Despite this promise, difficulty in assembling raw carbon nanotubes into functional structures is a deterrent for applications. In contrast, biological materials have evolved to self‐assemble, and the lessons of their self‐assembly can be applied to synthetic materials such as carbon nanotubes. Here we show that single‐walled carbon nanotubes, coated with a designed amphiphilic peptide, can be assembled into ordered hierarchical structures. This novel methodology offers a new route for controlling the physical properties of nanotube systems at all length scales from the nano‐ to the macroscale. Moreover, this technique is not limited to assembling carbon nanotubes, and could be modified to serve as a general procedure for controllably assembling other nanostructures into functional materials.