Inline cure monitoring of epoxy resin by dielectric analysis

Abstract

In the past years, epoxy resin molding compounds (EMCs) have gained in importance as packaging material in the field of electrification due to their very good mechanical and chemical resistance and electrical insulation properties. Forthcoming trends, such as the use of silicon carbide and ceramics, are accelerating developments and are leading to new requirements for encapsulation materials in terms of high temperature resistance and stability. A focus here is on epoxy resin molding compounds with high glass transition temperatures, which as a consequence of the limited manufacturing conditions of electronic components must be cured below their maximum glass transition temperature. As a result, setting-up the ideal curing process to achieve the desired material properties poses quite a challenge, making therefore the study of the cure behavior under process-related conditions a key factor to achieve the desired material properties. The dielectric analysis (DEA) is a powerful measurement technique well suited for in-situ monitoring of EMC curing in the field of direct packaging of electronic parts. However, it possesses one major drawback. The DEA has a systematic temperature dependence that hinders the determination of the cure state as it is usually known from standard offline measurement techniques such as thermomechanical analysis (TMA), or dynamic-scanning-calorimetry (DSC). In this work we present an empirical approach how to compensate the temperature influence of the dielectric analysis (DEA) for a commercially available high glass transition temperature (Tg) (higher $200^{\circ}\mathrm{C})$ EMC. This proposed normalization method allows to gain information about the cure state of an EMC under near process conditions what would already be a great advantage considering the future trends in electronic packaging technology. In addition, it opens new possibilities on how to expand the application of the DEA, e.g. kinetic cure characterization. The detailed material characterization and understanding can also lead to manufacturing optimization in terms of cost and development such as cycle times optimization and shorter development times.