Solder paste reflow modeling for flip chip assembly

Solder paste printing and reflow can provide low cost techniques producing the solder bumps on flip chips. Solder paste consists of a dense suspension of solder particles in a fluid medium (vehicle) that acts as an oxide reducing agent (flux) during reflow, cleaning the metal surfaces of oxides. This paper reports on optical observations of paste behaviour at the small length scales associated with flip chip solder joints, and attempts to model the process using Computational Fluid Dynamics (CFD). Comparison of optical observations and CFD modelling show that the behaviour of the solder cannot be described simply by surface tension and viscous flow effects and it is deduced that oxides are still present on the solder surfaces during the early stages of reflow. The implications for paste heating method and solder volume are discussed, and a preliminary CFD model (based on FIDAP) incorporating the effect of the oxide layers is presented

Computational modelling of the anisotropic conductive adhesive assembly process

Previously developed analytical models of the anisotropic adhesive assembly process have successfully predicted the time for adhesive resin flow out and whether this can be successfully achieved before resin cure. Computational Fluid Dynamics models have also provided significant insights into the effects of the component and substrate bond pad geometry on the resin flow distribution and hence on the resulting final conductive particle distribution. These computational models have however used Newtonian, i.e. non-shear thinning, flow properties for the adhesive materials. This paper will present initial results from the development of more sophisticated models, which include both the non-Newtonian and the temperature dependent flow of the adhesive. Such models can be used to allow a much more detailed investigation of the interactions of the adhesive resin flow characteristics, the component and substrate materials and geometry, and the assembly process parameters. These models, once fully developed and validated, will therefore lead to a better understanding of the assembly process and facilitate establishment of design rules for different application

The evolution of lung cancer and impact of subclonal selection in TRACERx.

Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource