Microwave cure of conductive adhesives for flip-chip & microsystems applications

The curing of conductive adhesives and underfills can save considerable time and offer cost benefits for the microsystems and electronics packaging industry. In contrast to conventional ovens, curing by microwave energy generates heat internally within each individual component of an assembly. The rate at which heat is generated is different for each of the components and depends on the material properties as well as the oven power and frequency. This leads to a very complex and transient thermal state, which is extremely difficult to measure experimentally. Conductive adhesives need to be raised to a minimum temperature to initiate the cross-linking of the resin polymers, whilst some advanced packaging materials currently under investigation impose a maximum temperature constraint to avoid damage. Thermal imagery equipment integrated with the microwave oven can offer some information on the thermal state but such data is based on the surface temperatures. This paper describes computational models that can simulate the internal temperatures within each component of an assembly including the critical region between the chip and substrate. The results obtained demonstrate that due to the small mass of adhesive used in the joints, the temperatures reached are highly dependent on the material properties of the adjacent chip and substrate.

Polymer cure modeling for microelectronics applications

A review of polymer cure models used in microelectronics packaging applications reveals no clear consensus of the chemical rate constants for the cure reactions, or even of an effective model. The problem lies in the contrast between the actual cure process, which involves a sequence of distinct chemical reactions, and the models, which typically assume only one, (or two with some restrictions on the independence of their characteristic constants.) The standard techniques to determine the model parameters are based on differential scanning calorimetry (DSC), which cannot distinguish between the reactions, and hence yields results useful only under the same conditions, which completely misses the point of modeling. The obvious solution is for manufacturers to provide the modeling parameters, but failing that, an alternative experimental technique is required to determine individual reaction parameters, e.g. Fourier transform infra-red spectroscopy (FTIR).

A critical analysis of polymer cure modeling for microelectronics applications

A review of polymer cure models used in microelectronics packaging applications reveals no clear consensus of the chemical rate constants for the cure reactions, or even of an effective model. The problem lies in the contrast between the actual cure process, which involves a sequence of distinct chemical reactions, and the models, which typically assume only one, (or two with some restrictions on the independence of their characteristic constants.) The standard techniques to determine the model parameters are based on differential scanning calorimetry (DSC), which cannot distinguish between the reactions, and hence yields results useful only under the same conditions, which completely misses the point of modeling. The obvious solution is for manufacturers to provide the modeling parameters, but failing that, an alternative experimental technique is required to determine individual reaction parameters, e.g. Fourier transform infra-red spectroscopy (FTIR).

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.