Investigation of Mechanical and Electrical Characteristics for Cracked Conductive Particle in Anisotropic Conductive Adhesive (ACA) Assembly

In an anisotropic conductive adhesive (ACA) assembly, the electrical conduction is usually achieved with the conductive particles between the bumps of integrated circuit (IC) and corresponding conductive tracks on the glass substrate. Fully understanding of the mechanical and electrical characteristics of ACA particles can help to optimize the assembly process and improve the reliability of ACA interconnection. Most conductive particles used in the ACA assembly are with cracks in the metal coating of the particles after the ACA bonding. This paper introduced the fracture analysis by applying the cohesive elements in the numerical model of the nickel-coated polymer particle and further simulating the cracks initiation and propagation in the nickel coating during the ACA bonding. The simulation results showed that the stress distribution on the nickel-coated particle with cracks was significantly different from that on the nickel-coated particle without crack, indicating that the stress analysis by taking the crack into consideration is very important for the reliability assessment of the ACA interconnection. The stress analysis of cohesive elements indicated that the cracks initiated at the central area of the nickel coating and propagated to the polar area. Furthermore, by the introduction of a new parameter of the virtual resistance, a mathematical model was established to describe the electrical characteristics of the nickel-coated particle with cracks. The particle resistance of the nickel-coated particle with cracks was found to be much higher than that of the particle without crack in the optimized bonding pressure range, indicating that it is necessary to take the crack into consideration for the particle conduction analysis as well. Therefore, the fracture analysis on the conductive particle by taking the crack into consideration could accurately evaluate the reliability of ACA interconnection and avoid serious reliability issues.     

Identifiability of nonlinear systems with application to HIV/AIDS models

In this note, we investigate different concepts of nonlinear identifiability in the generic sense. We work in the linear algebraic framework. Necessary and sufficient conditions are found for geometrical identifiability, algebraic identifiability and identifiability with known initial conditions. Relationships between different concepts are characterized. Constructive procedures are worked out for both generic geometrical and algebraic identifiability of nonlinear systems. As an application of the theory developed, we study the identifiability properties of a four dimensional model of HIV/AIDS. The questions answered in this study include the minimal number of measurement of the variables for a complete determination of all parameters and the best period of time to make such measurements. This information will be useful in formulating guidelines for clinical practice.     

Grain refinement of Al-7Si alloys and the efficiency assessment by recognition of cooling curves

Under sand cup solidification condition, the influence of Ti, B concentration, and holding time on the grain size of a high-purity Al-7Si alloy has been systematically studied. It is found that the grain size decreases rapidly at lower Ti or B additions, and is almost constant at the higher concentrations. For Al-3B refined Al-7Si alloys, the grain size increases at the initial period of holding time, and then rapidly becomes independent of holding time; while for Al-7Si alloys refined with Al-5Ti-1B, the grain size increases with holding time throughout. To evaluate the refinement efficiency, a new method named “intelligent evaluating of melt quality by pattern recognition of thermal analysis cooling curves” has also been introduced in this article. The results show that comparing the cooling curve of the evaluated melt with those in a database to find the most similar one to it can precisely assess the grain refinement efficiency of the measured Al-7Si alloy. In addition, the influence of Ti, B addition levels, and pouring temperatures on some characteristic parameters of cooling curve has also been discussed.     

Schwingfestigkeit von Schraube‐Mutter‐Verbindungen

High cycle fatigue of bolted connections Extensive tests regarding the influences on the fatigue of bolt‐nut‐connections of preloading with torsion, of preloading with yielding, of loading with superimposed bending and of the tested lot are processed. These influences are not yet known according to VDI 2230. New testing devices were designed for these tests, which allow a far less expensive operation and may easily be used for bolts of diameters up to M100 and testing frequencies up to 1000 Hz. The validity of fatigue resistance according to VDI 2230 is specified with respect to the test results. The determined influence of the tested lots is unexpectedly high. The manufacturing process of bolts should be improved to minimize this influence.     

Influence of Cu<Superscript>2+</Superscript> ions on structural and magnetic properties of NiZn ferrites

The paper is devoted to the preparation of NiZn ferrite with small substitutions of copper by means of ceramic technology. The influence of small Cu substitution on the microstructural and magnetic properties of NiZn ferrites have been analysed by means of various experimental methods and interpreted from the point of view of preparation technology optimisation and possible applications of such materials. A strong correlation between the substituent content and resulting properties has been observed, thus allowing preparation of material with the properties tailored for any particular application.     

Modeling of Temperature–Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique

A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, a method of combining principal component analysis (PCA) and support vector regression (SVR) technique is proposed for modeling temperature-caused variability of modal frequencies for structures instrumented with long-term monitoring systems. PCA is first applied to extract principal components from the measured temperatures for dimensionality reduction. The predominant feature vectors in conjunction with the measured modal frequencies are then fed into a support vector algorithm to formulate regression models that may take into account thermal inertia effect. The research is focused on proper selection of the hyperparameters to obtain SVR models with good generalization performance. A grid search method with cross validation and a heuristic method are utilized for determining the optimal values of SVR hyperparameters. The proposed method is compared with the method directly using measurement data to train SVR models and the multivariate linear regression (MLR) method through the use of long-term measurement data from a cable-stayed bridge. It is shown that PCA-compressed features make the training and validation of SVR models more efficient in both model accuracy and computational costs, and the formulated SVR model performs much better than the MLR model in generalization performance. When continuously measured data is available, the SVR model formulated taking into account thermal inertia effect can achieve more accurate prediction than that without considering thermal inertia effect.