The growing need for wearable devices, fitness accessories and biomedical equipment has led to the upsurge in research and development of thin flexible battery research and development. The current state of art wearable electronics products being developed in several fields require installation of power sources in different configurations and at times require the battery to undergo mechanical folding during product operation. This requires the product batteries to robustly withstand the imposed mechanical stresses during use along with the other desirable characteristics attributed to the power source such as high C-rate capability, high capacity and low capacity degradation rate. Works that explore the effects of static and dynamic folding on li-ion power sources is limited and oftentimes doesn’t adhere to definite test protocols resulting in non-standardized experimental data that can’t be applied to real-life product scenarios. Specifically, the effect of fold diameter on the battery state of health degradation when subjected to both static and dynamic folding is not yet completely explored. Present study aims to address this gap in the literature by investigating the effect of varying the fold diameter is both static (U-flex-to-install) and dynamic (dynamic U-fold) tests. Four different values of fold diameters have been chosen for experimentation and to study its effect during the aforementioned tests. Multiple samples have been tested for a given test condition so as to generate high fidelity data. Ultimately, a regression model developed previously has been augmented with the results generated in the current study.

The growing need for wearable devices, fitness accessories and biomedical equipment has led to the upsurge in research and development of thin flexible battery research and development. Wearable equipment and other asset monitoring applications require versatile installation of power sources on non-planar surfaces. For power sources in wearable electronics, perseverance towards repetitive mechanical stresses induced by human body motion is necessary along with the usual desirable characteristics such as high capacity, high C-rate capability and good life cycle stability. Prior studies which document the reliability of power sources subject to static and dynamic folding are scarce and at times fail to follow definitive test protocols which limit their application to real-life battery use scenarios. Particularly, the use of manual mechanical stressing of the power sources instead of a mechanical test setup is a key shortcoming in existing literature. Data is lacking on battery life cycling and in-situ mechanical stressing of the power sources including their impact of performance and reliability. Present study aims to overcome these deficiencies by testing a commercial Li-ion power source under static as well as dynamic folding. Furthermore, the fold-orientation and its fold-speed are varied to evaluate the effect of different mechanical stress topologies on the power source. Finally, a regression model was developed to capture the effect of these use parameters on battery capacity degradation.

Recent surge in demand for wearable technology products such as activity tracking smartwatches, and for medical devices has necessitated development of flexible secondary lithium ion batteries which also possess high capacity, robustness and thin form factors. Oftentimes, these power sources are only charged up to a partial state of charge (SoC) before use (shallow charge). Their usage continues until the SoC reaches almost zero, after which they are recharged again. Nowadays, the ‘fast-charge ‘feature used to charge the battery at higher C-rates, is a necessity in consumer electronics rather than an amenity. Also, in everyday use, these batteries are exposed to higher-than-ambient temperatures due to perpetual human body contact and also to the high temperatures resulting from poor thermal management in compact devices. This study investigates the compounded influence of partial charge, high temperatures and high C-rates on the capacity degradation of a flexible Li-ion power source subjected to accelerated life testing. The battery current and terminal voltage were logged for multiple charge-discharge cycles and were used to compute the battery capacity and energy efficiency. Finally, a regression model based on several parameters was developed to estimate the battery capacity as a function of the cycle number.

Decapsulation is one of the very powerful technique in failure analysis process. During this process, die and first level interconnects are exposed by dissolving molding compound around them using variety of methods. Typically decapsulation formulation uses red fuming nitric acid at elevated temperatures. This technique work for traditional Gold wire bonds, but does not work for its new alternative Copper. Gold, being inert metal does not react with acid. Copper on the other hand; tends to react with fuming nitric acid, and dissolves rapidly into acid. It is important to develop acid chemistry that can be successfully used to perform decapsulation of Cu-Al incorporated packages for different EMC’s. In this paper, decap process based on combination of red fuming nitric acid and concentrated sulfuric acid at elevated temperatures is presented. Reduction in wire diameter was monitored for all devices. For some devices decap process was evaluated based on comparison of WB shear strength of decaped part with unmolded part. SEM was used extensively to track down degradation of copper wires. These tests were performed on packages with different EMC’s, wire diameters, pad thickness and some active dies. Statistical principal components regression model has been developed correlating the decapsulation process parameters with the post decap wire diameter reduction. Principal component regression in conjunction with stepwise regression has been used to identify the influential variables, and to remove the multicollinearity between the predictor variables. Principal component analysis which combines two correlated variables into a single factor is a widely used image processing technique for pattern recognition and image compression. The post molded packages have then used to assess the effect of various decapsulation treatments.

Goldmann Constants and Norris-Landzberg acceleration factors for lead-free solders have been developed based on ridge regression models (RR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Ridge regression adds a small positive bias to the diagonal of the covariance matrix to prevent high sensitivity to variables that are correlated. The proposed procedure proves to be a better tool for prediction than multiple-linear regression models. Models have been developed in conjunction with Stepwise Regression Methods for identification of the main effects. Package architectures studied include, BGA packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermo-mechanical failure data. Data has been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation datasets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experiment study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined.

Goldmann Constants and Norris-Landzberg acceleration factors for lead-free solders have been developed based on principal component regression models (PCR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Models have been developed in conjunction with Stepwise Regression Methods for identification of the main effects. Package architectures studied include, BGA packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermo-mechanical failure data. Data has been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Goldmann Constants and the Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation data-sets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experiment study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages [Lall 2004, 2005], flip-chip packages [Lall 2005] and ceramic BGA packages [Lall 2007]. The presented methodology is valuable in the development of fatigue damage constants for the application specific accelerated test data-sets and provides a method to develop institutional learning based on prior accelerated test data.

In 3D-IC packages, the low- k dielectrics are favored to improve many electrical characteristics in IC devices; hence the corresponding researches are being widely studied extensively. For the purpose of studying thermal-mechanical effects on Cu/low- k interconnects in the wafer, the three dimensional finite element analysis (FEA) was adopted in this paper. Several low- k dielectrics were considered in FEA, which are SiLK, BCB and SiCOH. By using FEA, the corresponding thermal-mechanical induced stresses distribution of Cu/low- k interconnects would be obtained. Furthermore, in order to study the sensitivity information of Cu/low- k interconnects, the method of design of experiments (DOE) as well as factorial design methodology and response surface methodology were also used. Five major parameters of geometric dimensions of copper interconnects/vias structure were selected as the design factors and the maximum average value of von Mises stress in copper interconnects/vias structure was selected as the response to be optimized. Through the DOE analysis, the optimum geometric parameters of copper interconnects/vias structure that resulted in smaller von Mises stresses in Cu/low- k interconnects could be obtained. The results also show that the geometric dimension of copper interconnects/vias had significant influence on von Mises stresses distribution. Moreover, the maximum value of von Mises stress for different copper interconnects/vias geometric dimensions could also be easily predicted by using the response surface curve and its corresponding regression model from DOE analysis.