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Interposers with through-silicon vias (TSVs) play a key role in the three-dimensional integration and packaging of integrated circuits and microelectromechanical systems. In the current practice of fabricating interposers, solder balls are placed next to the vias; however, this approach requires a large foot print for the input/output (I/O) connections. Therefore, in this study, we investigate the possibility of placing the solder balls directly on top of the vias, thereby enabling a smaller pitch between the solder balls and an increased density of the I/O connections. To reach this goal, inkjet printing (that is, piezo and super inkjet) was used to successfully fill and planarize hollow metal TSVs with a dielectric polymer. The under bump metallization (UBM) pads were also successfully printed with inkjet technology on top of the polymer-filled vias, using either Ag or Au inks. The reliability of the TSV interposers was investigated by a temperature cycling stress test (−40°C to +125°C). The stress test showed no impact on DC resistance of the TSVs; however, shrinkage and delamination of the polymer was observed, along with some micro-cracks in the UBM pads. For proof of concept, SnAgCu-based solder balls were jetted on the UBM pads.

In this paper, we have studied the print parameter effects on electrohydrodynamic inkjet (E-jet) resolution using statistical analysis. In order to make the E-jet manufacturing process feasible, the effect of printing parameters on the ejected droplet size must be modelled and optimized. To this end, there exist two approaches: parameter effects can be modelled using theoretical calculations or they can be generated directly from empirical data using statistical analysis. The first option has been explored by multiple research groups, whereas the latter has received less interest. In this article, the effect of printing parameters on the width of AC-pulsed E-jet deposited Ag-nanoparticle ink droplets are investigated using design of experiments (DoE) approach and statistical analysis. As a result, a statistical model for deposited droplet width is generated using four print parameters (print height, bias voltage, peak voltage and frequency) as predictors. The model can predict 94.24% of the measured width variation with a standard deviation of 1.05 µm.