Using computer models to identify optimal conditions for flip-chip assembly and reliability

Flip-chip assembly, developed in the early 1960s, is now being positioned as a key joining technology to achieve high-density mounting of electronic components on to printed circuit boards for high-volume, low-cost products. Computer models are now being used early within the product design stage to ensure that optimal process conditions are used. These models capture the governing physics taking place during the assembly process and they can also predict relevant defects that may occur. Describes the application of computational modelling techniques that have the ability to predict a range of interacting physical phenomena associated with the manufacturing process. For example, in the flip-chip assembly process we have solder paste deposition, solder joint shape formation, heat transfer, solidification and thermal stress. Illustrates the application of modelling technology being used as part of a larger UK study aiming to establish a process route for high-volume, low-cost, sub-100-micron pitch flip-chip assembly.

Predicting optimal process conditions for flip-chip aassembly using copper column bumped dies

Recently, research has been carried out to test a novel bumping method which omits the under bump metallurgy (UBM) forming process by bonding copper columns directly onto the Al pads of the silicon dies. This bumping method could be adopted to simplify the flip chip assembly process, increase the productivity and achieve a higher I/O count. Computer modelling methods are used to predict the shape of solder joints and response of the flip chip to thermal cyclic loading. The accumulated plastic strain energy at the comer solder joints is used as the damage indicator. Models with a range of design parameters have been compared for their reliability. The ranking of the relative importance of these parameters is given. Results from these analyses are being used by our industrial and academic partners to identify optimal design conditions.

Application of particle swarm optimisation to evaluation of polymer cure kinetics models

A particle swarm optimisation approach is used to determine the accuracy and experimental relevance of six disparate cure kinetics models. The cure processes of two commercially available thermosetting polymer materials utilised in microelectronics manufacturing applications have been studied using a differential scanning calorimetry system. Numerical models have been fitted to the experimental data using a particle swarm optimisation algorithm which enables the ultimate accuracy of each of the models to be determined. The particle swarm optimisation approach to model fitting proves to be relatively rapid and effective in determining the optimal coefficient set for the cure kinetics models. Results indicate that the singlestep autocatalytic model is able to represent the curing process more accurately than more complex model, with ultimate accuracy likely to be limited by inaccuracies in the processing of the experimental data.