Microstructural Evolution and Some Unusual Effects During Thermo-Mechanical Cycling of Sn-Ag-Cu Alloys

Sn-Ag-Cu (SAC) solders are susceptible to appreciable microstructural coarsening during storage or service. This results in evolution of joint properties over time, and thereby influences the long-term reliability of microelectronic packages. Accurate prediction of this aging behavior is therefore critical for joint reliability predictions. Here, we study the precipitate coarsening behavior in two Sn-Ag-Cu (SAC) alloys, namely Sn-3.0Ag-0.5Cu and Sn-1.0Cu-0.5Cu, under different thermo-mechanical excursions, including isothermal aging at 150 degrees C for various lengths of time and thermo-mechanical cycling between -25 degrees C and 125 degrees C, with an imposed shear strain of similar to 19.6% per cycle, for different number of cycles. During isothermal aging and the thermo-mechanical cycling up to 200 cycles, Ag3Sn precipitates undergo rapid, monotonous coarsening. However, high number of thermo-mechanical cycling, usually between 200 and 600 cycles, causes dissolution and re-precipitation of precipitates, resulting in a fine and even distribution. Also, recrystallization of Sn-grains near precipitate clusters was observed during severe isothermal aging. Such responses are quite unusual for SAC solder alloys. In the regime of usual precipitate coarsening in these SAC alloys, an explicit parameter, which captures the thermo-mechanical history dependence of Ag3Sn particle size, was defined. Brief mechanistic description for the recrystallization of Sn grains during isothermal aging and reprecipitation of the Ag3Sn due to high number of thermo-mechanical cycles are also presented.

Predicting the lithospheric stress field and plate motions by joint modeling of lithosphere and mantle dynamics

The way in which basal tractions, associated with mantle convection, couples with the lithosphere is a fundamental problem in geodynamics. A successful lithosphere-mantle coupling model for the Earth will satisfy observations of plate motions, intraplate stresses, and the plate boundary zone deformation. We solve the depth integrated three-dimensional force balance equations in a global finite element model that takes into account effects of both topography and shallow lithosphere structure as well as tractions originating from deeper mantle convection. The contribution from topography and lithosphere structure is estimated by calculating gravitational potential energy differences. The basal tractions are derived from a fully dynamic flow model with both radial and lateral viscosity variations. We simultaneously fit stresses and plate motions in order to delineate a best-fit lithosphere-mantle coupling model. We use both the World Stress Map and the Global Strain Rate Model to constrain the models. We find that a strongly coupled model with a stiff lithosphere and 3-4 orders of lateral viscosity variations in the lithosphere are best able to match the observational constraints. Our predicted deviatoric stresses, which are dominated by contribution from mantle tractions, range between 20-70 MPa. The best-fitting coupled models predict strain rates that are consistent with observations. That is, the intraplate areas are nearly rigid whereas plate boundaries and some other continental deformation zones display high strain rates. Comparison of mantle tractions and surface velocities indicate that in most areas tractions are driving, although in a few regions, including western North America, tractions are resistive. Citation: Ghosh, A., W. E. Holt, and L. M. Wen (2013), Predicting the lithospheric stress field and plate motions by joint modeling of lithosphere and mantle dynamics.

Is Granger Causality a Viable Technique for Analyzing fMRI Data?

Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI) data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a) latency differences in hemodynamic response function (HRF) across different brain regions, (b) low-sampling rates, and (c) noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC) between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1) GC following HRF convolution is a monotonically increasing function of neural GC; (2) this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3) although the detectability of monotonicity declined due to the presence of HRF latency differences, substantial recovery of detectability occurred after correcting for latency differences. These results suggest that Granger causality is a viable technique for analyzing fMRI data when the questions are appropriately formulated.

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix.

Tailor-Made Hollow Silver Nanoparticle Cages Assembled with Silver Nanoparticles: An Efficient Catalyst for Epoxidation

A novel approach toward the synthesis of hollow silver nanoparticle (NP) cages built with building blocks of silver NPs by layer-by-layer (LbL) assembly is demonstrated. The size of the NP cage depends on the size of template used for the LbL assembly. The microcages showed a uniform distribution of spherical silver nanoparticles with an average diameter of 20 +/- 5 nm, which increased to 40 +/- S nm when the AgNO3 concentration was increased from 25 to 50 mM. Heat treatment of the polyelectrolyte capsules at 80 degrees C near their pK(a) values yielded intact nano/micro cages. These cages produced a higher conversion for the epoxidation of olefins and maintained their catalytic activity even after four successive uses. The nanocages exhibited unique and attractive characteristics for metal catalytic systems, thus offering the scope for further development as heterogeneous catalysts.

Nonequilibrium ``Melting'' of a Charge Density Wave Insulator via an Ultrafast Laser Pulse

We employ an exact solution of the simplest model for pump-probe time-resolved photoemission spectroscopy in charge-density-wave systems to show how, in nonequilibrium, the gap in the density of states disappears while the charge density remains modulated, and then the gap reforms after the pulse has passed. This nonequilibrium scenario qualitatively describes the common short-time experimental features in TaS2 and TbTe3, indicating a quasiuniversality for nonequilibrium ``melting'' with qualitative features that can be easily understood within a simple picture.

Energy Efficient GPS Acquisition with Sparse-GPS

Following rising demands in positioning with GPS, low-cost receivers are becoming widely available; but their energy demands are still too high. For energy efficient GPS sensing in delay-tolerant applications, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose Sparse-GPS(1): a new computing framework for GPS acquisition via sparse approximation. Within the framework, GPS signals can be efficiently compressed by random ensembles. The sparse acquisition information, pertaining to the visible satellites that are embedded within these limited measurements, can subsequently be recovered by our proposed representation dictionary. By extensive empirical evaluations, we demonstrate the acquisition quality and energy gains of Sparse-GPS. We show that it is twice as energy efficient than offloading uncompressed data, and has 5-10 times lower energy costs than standalone GPS; with a median positioning accuracy of 40 m.

Sparse Representation Based Acoustic Rangefinders: From Sensor Platforms to Mobile Devices

Acoustic rangerfinders are a promising technology for accurate proximity detection, a critical requirement for many emerging mobile computing applications. While state-of-the-art systems deliver robust ranging performance, the computational intensiveness of their detection mechanism expedites the energy depletion of the associated devices that are typically powered by batteries. The contribution of this article is fourfold. First, it outlines the common factors that are important for ranging. Second, it presents a review of acoustic rangers and identifies their potential problems. Third, it explores the design of an information processing framework based on sparse representation that could potentially address existing challenges, especially for mobile devices. Finally, it presents mu-BeepBeep: a low energy acoustic ranging service for mobile devices, and empirically evaluates its benefits.