Thermal Design of Three-Dimensional Electronic Assemblies

Currently, three-dimensional electronic assemblies (3D Packages) are a key technology for enabling heterogeneous integration and “more than Moore” functionality. A critical bottleneck to the viability of 3D Packages is their thermal design. Traditionally, heat spreaders are used as a passive method to reduce the peak temperature as well as temperature gradient on the chip. However, heat spreaders by themselves are often insufficient in stacked, multiple-die containing 3D Packages. Towards this end, to more efficiently remove heat, silicon interposers with through silicon vias (TSV) are used. However, careful design of number and location of TSVs is necessary. In addition, the heat spreader design as well as the selection of thermal interface materials needs careful consideration. At the present time, there are no automated tools available to carryout such a thermal design of 3D Packages. The present study is focused on the development of an efficient tool that determines the optimal configuration of heat spreading elements subject to constraints on allowable copper heat spreading area or metal volume. To achieve this goal, a three-dimensional finite element analysis (FEA) code for steady-state heat conduction is coupled with a sequential quadratic programming (SQP) algorithm, and both are implemented within the MATLAB environment. Considerable effort was spent to ensure efficient matrix solution using a sparse matrix solver during FEA. Several example problems are solved and the results are compared against solutions obtained using Simulia iSight in combination with the sophisticated Simulia ABAQUS FEA tool. The developed tool is demonstrated to be nearly two-orders of magnitude faster for the same level of accuracy in the final solution

Inversion technique for quantitative infrared thermography evaluation of delamination defects in multilayered structures

Inverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without pre-inspections for characterization criteria. For traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modelling simulation is very time-consuming. Such issues result in ill-posed and complex inverse reconstruction results, which further reduces its practical applicability. In this paper, we propose and experimentally validate an inversion technique for the reconstruction of complexly-shaped delamination defects in a multilayered metallic structure using signals derived from infrared thermography (IRT) testing. First, we employ a novel defect parameterization strategy based on Fourier series fitting to represent the profile of a complicated delamination defect with relatively few coefficients. Secondly, the multi-medium element modelling method is applied to enhance a FEM fast forward simulator, in order to solve the mismatching mesh issue for mesh updating during inversion. Thirdly, a deterministic inverse algorithm based on a penalty conjugate gradient algorithm is employed to realize a robust and efficient inverse analysis. By reconstructing delamination profiles with both numerically-simulated IRT signals and those obtained through laser IRT experiments, the validity, efficiency and robustness of the proposed inversion method are demonstrated for delamination defects in a double-layered plate. Based on this strategy, not only is the feasibility of the proposed method in Infrared thermography NDT validated, but the practical applicability of inversion reconstruction analysis is significantly improved

Machine learning reveals cryptic dialects that explain mate choice in a songbird

Culturally transmitted communication signals – such as human language or bird song – can change over time through cultural drift, and the resulting dialects may consequently enhance the separation of populations. However, the emergence of song dialects has been considered unlikely when songs are highly individual-specific, as in the zebra finch (Taeniopygia guttata). Here we show that machine learning can nevertheless distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that ‘cryptic song dialects’ predict strong assortative mating in this species. We examine mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. We cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions. We find that females preferentially pair with males whose song resembles that of the females’ adolescent peers. Our study shows evidence that in zebra finches, a model species for song learning, individuals are sensitive to differences in song that have hitherto remained unnoticed by researchers

Degradation Science: Mesoscopic Evolution and Temporal Analytics of Photovoltaic Energy Materials

Based on recent advances in nanoscience, data science and the availability of massive real-world datastreams, the mesoscopic evolution of mesoscopic energy materials can now be more fully studied. The temporal evolution is vastly complex in time and length scales and is fundamentally challenging to scientific understanding of degradation mechanisms and pathways responsible for energy materials evolution over lifetime. We propose a paradigm shift towards mesoscopic evolution modeling, based on physical and statistical models, that would integrate laboratory studies and real-world massive datastreams into a stress/mechanism/response framework with predictive capabilities. These epidemiological studies encompass the variability in properties that affect performance of material ensembles. Mesoscopic evolution modeling is shown to encompass the heterogeneity of these materials and systems, and enables the discrimination of the fast dynamics of their functional use and the slow and/or rare events of their degradation. We delineate paths forward for degradation science

Novel genotypes and phenotypes in Snijders Blok-Campeau syndrome caused by CHD3 mutations

BackgroundSnijders Blok-Campeau syndrome (SNIBCPS) is a rare genetic disorder characterized by facial abnormalities, hypotonia, macrocephaly, and global developmental delay (GDD) caused by mutations in CHD3 gene. There is limited information on SNIBCPS and few studies on its pathogenic gene CHD3.MethodsWe utilized whole-exome sequencing, in vitro minigene splicing assay analysis, and construction of protein models to validate the suspected pathogenic mutation. In addition, the PubMed database was searched using the keywords “Snijders Blok-Campeau syndrome,” “CHD3,” or “SNIBCPS” to summarize the gene mutations and clinical phenotypic characteristics of children with SNIBCPS.ResultsWe identified a non-frameshift variant c.3592_c.3606delGCCAAGAGAAAGATG, a splice site variant c.1708-1G>T, and two missense variants, c. 2954G>C (p.Arg985Pro) and c.3371C>T (p.A1124V), in CHD3 variants with SNIBCPS. Importantly, the c.3592_c.3606delGCCAAGAGAAAGATG, c.1708-1G>T, and c.3371C > T (p.A1124V) loci were not reported, and the children in this study also had phenotypic features of unibrow, transverse palmar creases, tracheal bronchus, and hypomelanosis of Ito (HI). The c.1708-1G>T classical splicing mutation leads to abnormal shearing of mRNA, forming a truncated protein that ultimately affects gene function.ConclusionOur findings have expanded the spectrum of genetic variants and clinical features in children with SNIBCPS. Splicing analysis of CHD3 is an important method to understand the pathogenesis of spliced cells

The Effect of Different Trigger Thresholds on the Quality of Pulmonary Artery CT Angiography Images

Objective: To study the effect of different triggering thresholds on the quality of pulmonary artery CT angiography (CTA) images. Materials and Methods: A prospective study included 112 patients with suspected pulmonary embolism admitted to Tinglin Hospital in the Jinshan District of Shanghai between December 2021 to April 2023. Among them, there were 49 males and 63 females aged between 37 and 93 years, with an average age of 64.28 years. Patients were randomly assigned to three groups based on trigger thresholds. Group A included 38 cases with a trigger threshold of 120 HU, Group B included 37 cases with a trigger threshold of 200 HU, and Group C included 37 cases with a trigger threshold of 250 HU. There were no statistically significant differences in gender, age, height, or weight among the three groups. One-way ANOVA was used to compare the CT values and subjective image quality scores of the superior vena cava, main pulmonary artery, left and right pulmonary arteries, and right pulmonary vein among the three groups. Result: There were no statistical differences in the CT values of the main pulmonary artery and left and right pulmonary arteries among the three groups, but there were statistical differences in the CT values of the superior vena cava and right pulmonary vein. There was a statistical difference in the subjective score of image quality among the three groups; the subjective evaluation of the obtained image quality between the two physicians was highly consistent (κ=0.78). Conclusion: When the triggering threshold of pulmonary artery CTA is 200 HU, it can not only ensure the concentration of pulmonary artery trunk CT value meets the clinical diagnosis, but also ensures that the contrast agent is fully injected into the 5~6 grade branches, leading to less retention of superior vena cava, weak pulmonary vein development, and the highest image quality of pulmonary artery CTA