The aim of this research is to understand the failure modes and mechanisms of adhesive materials used to flip-chip bond a silicon die onto a polyimide substrate. The bonding material investigated in this research is called Anisotropic Conductive Film (ACF). This is a promising interconnection material and has gained extensive interest in the electronics packaging industry. Both the experimental and finite element analysis (FEA) methods were used in order to investigate the behaviour of the ACF materials when subjected to certain manufacturing and environmental testing conditions. The manufacturing condition investigated was a subsequent solder reflow process on an ACF flip-chip bonded device. The environmental testing condition investigated was the moisture test. For the manufacturing condition, both experimental and modelling results demonstrate the impact of a subsequent reflow process on the behaviour of the ACF joint. Typical failures observed after this process were cracks at the pad/particle interface. This failure mode was more sever with a higher peak reflow temperature. This was also found using FEA where high tensile stresses were predicted in these regions. FEA modelling was also used to help identify the mechanisms leading to these failures. This is primarily due to the Coefficient of Thermal Expansion (CTE) miss-match in the materials and the elastic/plastic deformation behaviour of the conductive particle. Important design variables that can minimise these failures are the Young’s Modulus and CTE of the adhesive and the height of the hump on the die. For the environmental testing condition, an autoclave test at 121°C, 100%RH and pressure of 2atm was used. More than 85% of the ACF joints failed during the first 24 hours of testing. The failure mode observed was cracking along the interface between the adhesive and substrate and pad. A macro-micro modelling approach was used to help identify the mechanisms leading to these failures. It was found that most of the damage is caused by moisture diffusion and associated swelling. Important design variables that will help minimise this mode of failure are: Coefficient of Moisture Expansion (CME) and Young’s Modulus of the adhesive and the height of the bump on the die.

This thesis investigates the feasibility of establishing a generalised approach for defining similarity metrics between 3D shapes for the casting design problem in Case-Based Reasoning (CBR). This research investigates a new approach for improving the quality of casting design advice achieved from a CBR system using casting design knowledge associated with past cases. The new approach uses enhanced similarity metrics to those used in previous research in this area to achieve improvements in the advice given. The new similarity metrics proposed here are based on the decomposition of casting shape cases into a set of components. The research into metrics defines and uses the Component Type Similarity Metric (CTM) and Maximum Common Subgraph (MCS) metric between graph representations of the case shapes and are focused on the definition of partial similarity between the components of the same type that take into account the geometrical features and proportions of each single shape component. Additionally, the investigation extends the scope of the research to 3D shapes by defining and evaluating a new metric for the overall similarity between 3D shapes. Additionally, this research investigates a methodology for the integration of the CBR cycle and automation of the feature extraction from target and source case shapes. The ShapeCBR system has been developed to demonstrate the feasibility of integrating the CBR approach for retrieving and reusing casting design advice. The ShapeCBR system automates the decomposition process, the classification process and the shape matching process and is used to evaluate the new similarity metrics proposed in this research and the extension of the approach to 3D shapes. Evaluation of the new similarity metrics show that the efficiency of the system is enhanced using the new similarity metrics and that the new approach provides useful casting design information for 3D casting shapes. Additionally, ShapeCBR shows that it is possible to automate the decomposition and classification of components that allow a case shape to be represented in graph form and thus provide the basis for automating the overall CBR cycle. The thesis concludes with new research questions that emerge from this research and an agenda for further work to be pursued in further research in the area.