Design of Application-Specific Architectures for Networked Labs-on-Chips

Andreas Grimmer, Werner Haselmayr, Andreas Springer, Robert Wille

Research output: Contribution to journalArticlepeer-review

Abstract

Labs-on-Chips (LoCs) implement laboratory procedures on a single chip and are successfully used for chemical and biomedical applications. A promising and emerging realization of such chips are Networked LoCs (NLoCs) in which small volumes of fluids, so-called droplets, flow in closed channels of submillimeter diameters. NLoCs allow for an incubation and storage of assays over a long period of time and, hence, avoid evaporation and unwanted reactions. To increase the flexibility, effectiveness, and reusability, network functionalities allow to passively route droplets in channels and, hence, to dynamically select operations depending on the executed experiment. However, only manually designed architectures are considered for NLoCs thus far. They frequently suffer from large execution times and/or a high contamination of channels. To overcome these drawbacks, we propose the consideration of application-specific architectures for NLoCs. To this end, an automatic design method is proposed which, for a given set of experiments as well as constraints and objectives from the designer, is able to generate an optimized NLoC architecture realizing these experiments. Evaluations and case studies demonstrate the potential of the proposed solution for design exploration. Moreover, we are able to show that application-specific architectures are capable of realizing experiments in just a fraction of the time needed by architectures used thus far as well as with a substantially reduced contamination.
Original languageEnglish
Pages (from-to)193-202
Number of pages10
JournalIEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (TCAD)
Volume37
Issue number1
DOIs
Publication statusPublished - 2018

Fields of science

  • 102 Computer Sciences
  • 202 Electrical Engineering, Electronics, Information Engineering

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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