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Related Experiment Video

Updated: Dec 23, 2025

Rapid Fabrication of Custom Microfluidic Devices for Research and Educational Applications
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Computer-Aided Design of Microfluidic Circuits.

Elishai Ezra Tsur1

  • 1Neuro-Biomorphic Engineering Lab (NBEL), Department of Mathematics and Computer Science, The Open University of Israel, Ra'anana 4353701, Israel;

Annual Review of Biomedical Engineering
|April 29, 2020
PubMed
Summary

Computer-aided design (CAD) advances microfluidic circuits with new algorithms and tools for flow, droplet, and paper-based systems. Future directions include cloud computing and machine learning for enhanced microfluidic device development.

Keywords:
continuous flow-based microfluidicsdesign automationmicrofluidicsoptimization algorithmsresistive microfluidic networks

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Area of Science:

  • Microfluidics Engineering
  • Computational Science
  • Materials Science

Background:

  • Microfluidic devices have grown in complexity, demanding advanced performance, novel materials, and innovative fabrication techniques.
  • This complexity necessitates new algorithmic and design approaches for microfluidic circuit optimization and computer-aided design (CAD).

Purpose of the Study:

  • To review recent advancements in the computer-aided design of various microfluidic systems.
  • To provide a detailed case study on the design of resistive microfluidic networks.
  • To offer perspectives on the future trajectory of CAD in microfluidics.

Main Methods:

  • Review of recent literature on CAD for microfluidics.
  • Detailed case study analysis of resistive microfluidic network design.
  • Exploration of emerging technologies and methodologies in the field.

Main Results:

  • Significant progress in CAD for flow-, droplet-, and paper-based microfluidics.
  • Demonstration of a comprehensive design process for resistive microfluidic networks.
  • Identification of key areas for future development in microfluidic CAD.

Conclusions:

  • CAD is crucial for optimizing intricate microfluidic systems.
  • Emerging technologies like cloud computing and machine learning will shape future microfluidic design.
  • Continued innovation in algorithms, tools, and ideation processes is expected.