A microvia-fill model is formulated for control-design purposes of the via-fill process in 100 m scale. The formulas required to model a Cu-Cu-electrode electroplating system with generic additive-type chemicals in both single-directional and bidirectional processes are given in detail. The model relies on principles familiar from a submicrometer-scale via-filling model known as the curvature-enhanced accelerator accumulation model. Additive coverage is modeled based on a simplified method based on local surface-area computation and an analytical formula for surfactant coverage is given. A galvanostatic control law that does not require computing cell voltage is derived. The model also considers the Cu͑II͒-ion activity vs Cu͑II͒-ion concentration. A finite element method-based implementation, applying the arbitrary Lagrange-Eulerian method to compute geometry changes, is tested and compared to via-fill experiment results.
A computational model for examining the microvia fill process as encountered in the multilayered printed circuit board industry is presented. The model includes mass transfer of both copper and the additive species present. The additives' mass balance is considered between both the solution and the surface-adsorbed layer of additives, as well as on the shape-changing cathode surface where the mass balance of adsorbed additives is affected by the surface shape change and diffusive mass transfer along the surface. The model is implemented as a finite element model applying the arbitrary Lagrange-Eulerian ͑ALE͒ method for boundary tracking, and a weak formulation of the mass-balance equations is given to aid numerical solution of the model. Model stability and fitting against experimental data is examined over a range of relevant parameters.
The behavior of the maximum temperature measured inside a SOFC stack with respect to three independent input variables (stack current, air flow and air inlet temperature) is examined by using a full factorial screening experiment, following the design of experiments methodology. The experiments were carried out with a complete 10 kW e SOFC system. Multivariate regression models are developed to estimate said temperature and a statistical analysis is carried out on the model parameters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.