Antti Pohjoranta

Microvia fill process model and control

This paper presents an overview of the modeling and model based control of the microvia fill process. A detailed, general-purpose microvia fill model that utilizes the arbitrary Lagrange–Eulerian method for shape change modeling is presented first. Modeling results are presented and compared with the measured data. Then a simplified model of the process is presented followed by a model based control algorithm being developed based on the simplified stochastic model with typical uncertainties induced by unmodelled phenomena and measurement error. The developed control algorithm is analyzed in terms of possible control errors and applied to control the firstly presented microvia fill process model.

SOFC modelling based on discrete Bayesian network For system diagnosis use

Abstract We propose in this paper a diagnosis method that is aimed to detect and isolate SOFC system fault by using the FC stack as a sensor. A discrete Bayesian network (BN) was established to illustrate the input-output causal relations of the stack. In order to examine the generalizability of the network structure, the BN was parameterized to fit the experimental data from two different SOFC systems. The models showed reasonable accuracy of state estimation for 6 operating variables. Finally, the BN model was experimented for diagnosing a specified system fault.

Microvia Fill Process Boundary Control

Abstract This paper presents an exponentially stabilizing boundary control for the microvia fill process. The control accounts for the mass balance of the copper ions in the electrolyte and for the surface mass balance of the deposition-blocking additives, both modeled with a diffusion mass transfer model in a shape changing domain. With simulations based on real-world data, it is shown that by applying the control, the microvia fill process can be speeded up (in the example case by ca. 15%) without endangering product output quality.