research-article

Novel Wire Planning Schemes for Pin Minimization in Digital Microfluidic Biochips

Authors:

Hafizur Rahaman,

Parthasarathi DasguptaAuthors Info & Claims

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume 24, Issue 11

Pages 3345 - 3358

https://doi.org/10.1109/TVLSI.2016.2541671

Published: 01 November 2016 Publication History

Abstract

Digital microfluidic biochips (DMFBs), a second-generation lab-on-chip device has developed in recent years as a feasible alternative to conventional laboratory procedure for biochemical analysis and diagnostic applications. These devices enable the precise manipulation of nanoliter volumes of biological fluids and chemical reagents within a 2-D rectangular array of electrodes. Increasing number of control pin requirements together with high wire planning complexity becomes a major issue for integrated execution of multiple bioassays within a single DMFB layout. In this paper, we propose new techniques for interconnection wire routing for actuating the electrodes operating at identical time sequence. Here, we propose three different algorithms to develop feasible wire plans for a given layout with an aim to minimize the overall number of control pin count. Multiple actuation on the same pin termed multiphasing is proposed to resolve the issue of wire planning for handling the issue of cross contamination at any particular site. The proposed techniques are implemented in layouts using testbenches for benchmark suite III and selective testbenches for benchmark suite I. The results obtained through simulation show encouraging improvement over contemporary contributions.

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Cited By

Datta PChakraborty APal R(2020)A Predictive Model for Fluid-Control Codesign of Paper-Based Digital Biochips Following a Machine Learning ApproachIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2020.303050128:12(2584-2597)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1109/TVLSI.2020.3030501

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cover image IEEE Transactions on Very Large Scale Integration (VLSI) Systems

IEEE Transactions on Very Large Scale Integration (VLSI) Systems Volume 24, Issue 11

November 2016

180 pages

ISSN:1063-8210

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Copyright © 2016.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 November 2016

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Cited By

Datta PChakraborty APal R(2020)A Predictive Model for Fluid-Control Codesign of Paper-Based Digital Biochips Following a Machine Learning ApproachIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2020.303050128:12(2584-2597)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1109/TVLSI.2020.3030501

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