Modeling ceramic filled polymer integrated capacitor formation using neural networks

Integrated decoupling capacitors for MCM-L/D technology are an important component for next-generation electronic packaging applications. This paper presents a statistically designed experiment for systematic characterization of the dielectric constant and loss tangent of integrated capacitors formed by mixing lead magnesium niobate (PMN) particles into polyimide and benzocyclobutene (BCB) polymer dielectric layers. We determine these quantities as a function of the type of polymer material, a volume fraction of ceramic in the polymer matrix, a polymer cure time, and polymer cure temperature. These factors have been examined by means of a D-optimal experiment. Results indicate manipulation of each of the four factors over the ranges examined lead to considerable variation in dielectric constant and loss tangent. Based on data from these experiments, we train neural networks to model this process variation as a function of above variables. Using this methodology, we determine proper combinations of polymer/ceramic materials and processing conditions to achieve desirable electrical properties.