Surface Insulation Resistance Degradation and Electrochemical Migration on Printed Circuit Boards

Widespread adoption of lead-free materials and processing for printed circuit board (PCB) assembly has raised reliability concerns regarding surface insulation resistance (SIR) degradation and electrochemical migration (ECM). As PCB conductor spacings decrease, electronic products become more susceptible to these failures mechanisms, especially in the presence of surface contamination and flux residues which might remain after no-clean processing. Moreover, the probability of failure due to SIR degradation and ECM is affected by the interaction between physical factors (such as temperature, relative humidity, electric field) and chemical factors (such as solder alloy, substrate material, no-clean processing). Current industry standards for assessing SIR reliability are designed to serve as short-term qualification tests, typically lasting 72 to 168 hours, and do not provide a prediction of reliability in long-term applications. The risk of electrochemical migration with lead-free assemblies has not been adequately investigated. Furthermore, the mechanism of electrochemical migration is not completely understood. For example, the role of path formation has not been discussed in previous studies. Another issue is that there are very few studies on development of rapid assessment methodologies for characterizing materials such as solder flux with respect to their potential for promoting ECM. In this dissertation, the following research accomplishments are described: 1). Long-term temp-humidity-bias (THB) testing over 8,000 hours assessing the reliability of printed circuit boards processed with a variety of lead-free solder pastes, solder pad finishes, and substrates. 2). Identification of silver migration from Sn3.5Ag and Sn3.0Ag0.5Cu lead-free solder, which is a completely new finding compared with previous research. 3). Established the role of path formation as a step in the ECM process, and provided clarification of the sequence of individual steps in the mechanism of ECM: path formation, electrodeposition, ion transport, electrodeposition, and filament formation. 4). Developed appropriate accelerated testing conditions for assessing the no-clean processed PCBs' susceptibility to ECM: a). Conductor spacings in test structures should be reduced in order to reflect the trend of higher density electronics and the effect of path formation, independent of electric field, on the time-to-failure. b). THB testing temperatures should be modified according to the material present on the PCB, since testing at 85oC can cause the evaporation of weak organic acids (WOAs) in the flux residues, leading one to underestimate the risk of ECM. 5). Correlated temp-humidity-bias testing with ion chromatography analysis and potentiostat measurement to develop an efficient and effective assessment methodology to characterize the effect of no-clean processing on ECM.

Assessing the influence of abiotic factors and leaf-level properties on the stability of growing-season canopy greenness in a deciduous forest

Maps depicting spatial pattern in the stability of summer greenness could advance understanding of how forest ecosystems will respond to global changes such as a longer growing season. Declining summer greenness, or “greendown”, is spectrally related to declining near-infrared reflectance and is observed in most remote sensing time series to begin shortly after peak greenness at the end of spring and extend until the beginning of leaf coloration in autumn,. Understanding spatial patterns in the strength of greendown has recently become possible with the advancement of Landsat phenology products, which show that greendown patterns vary at scales appropriate for linking these patterns to proposed environmental forcing factors. This study tested two non-mutually exclusive hypotheses for how leaf measurements and environmental factors correlate with greendown and decreasing NIR reflectance across sites. At the landscape scale, we used linear regression to test the effects of maximum greenness, elevation, slope, aspect, solar irradiance and canopy rugosity on greendown. Secondly, we used leaf chemical traits and reflectance observations to test the effect of nitrogen availability and intrinsic water use efficiency on leaf-level greendown, and landscape-level greendown measured from Landsat. The study was conducted using Quercus alba canopies across 21 sites of an eastern deciduous forest in North America between June and August 2014. Our linear model explained greendown variance with an R2=0.47 with maximum greenness as the greatest model effect. Subsequent models excluding one model effect revealed elevation and aspect were the two topographic factors that explained the greatest amount of greendown variance. Regression results also demonstrated important interactions between all three variables, with the greatest interaction showing that aspect had greater influence on greendown at sites with steeper slopes. Leaf-level reflectance was correlated with foliar δ13C (proxy for intrinsic water use efficiency), but foliar δ13C did not translate into correlations with landscape-level variation in greendown from Landsat. Therefore, we conclude that Landsat greendown is primarily indicative of landscape position, with a small effect of canopy structure, and no measureable effect of leaf reflectance. With this understanding of Landsat greendown we can better explain the effects of landscape factors on vegetation reflectance and perhaps on phenology, which would be very useful for studying phenology in the context of global climate change

THE DISTRIBUTION AND FUNCTION OF DENITRIFICATION GENES: EXPLORING AGRICULTURAL MANAGEMENT AND SOIL CHEMICAL IMPLICATIONS

Denitrification is a microbially-mediated process that converts nitrate (NO3-) to dinitrogen (N2) gas and has implications for soil fertility, climate change, and water quality. Using PCR, qPCR, and T-RFLP, the effects of environmental drivers and land management on the abundance and composition of functional genes were investigated. Environmental variables affecting gene abundance were soil type, soil depth, nitrogen concentrations, soil moisture, and pH, although each gene was unique in its spatial distribution and controlling factors. The inclusion of microbial variables, specifically genotype and gene abundance, improved denitrification models and highlights the benefit of including microbial data in modeling denitrification. Along with some evidence of niche selection, I show that nirS is a good predictor of denitrification enzyme activity (DEA) and N2O:N2 ratio, especially in alkaline and wetland soils. nirK was correlated to N2O production and became a stronger predictor of DEA in acidic soils, indicating that nirK and nirS are not ecologically redundant.