Robustness Analysis for Droplet-Based Microfluidic Networks

Gerold Fink, Andreas Grimmer, Medina Hamidovic, Werner Haselmayr, Robert Wille

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Microfluidic networks can be applied to droplet-based Lab-on-a-Chip devices, where droplets are used to confine samples which flow through closed microchannels along different paths in order to execute (bio-)chemical experiments. In order to allow this routing of droplets, the design of the microfluidic network has to be precisely defined and afterward fabricated. However, neither the fabrication process nor the applied materials and components are perfect and, therefore, the fabricated microfluidic device frequently contains defects (produced by fabrication tolerances, properties of the used material, or fluctuation of supply pumps). Those may have a severe impact on the behavior of the microfluidic network and can even render the network useless. Furthermore, these defects complicate the design process, which eventually results in a 'trial-and-error'-approach causing high costs with respect to time and money. Consequently, designers want to anticipate how robust their design is against those defects. This article, for the first time, describes how these defects can be abstracted, which eventually allows to evaluate the robustness already in the design process. We additionally introduce models considering single and multiple defects as well as corresponding methods for their analysis. Evaluations on a microfluidic network which is used to screen drug compounds confirm that the resulting robustness analysis indeed provides designers with a simple metric to decide how sensitive their design is against defects. The models and methods proposed in this article are grounded on the established 1-D analysis model.

Original languageEnglish
Article number8944309
Pages (from-to)2696-2707
Number of pages12
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number10
StatePublished - Oct 2020
Externally publishedYes


  • Droplet microfluidics
  • microfluidic networks
  • robustness


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