A Predictive Model for Fluid-Control Codesign of Paper-Based Digital Biochips Following a Machine Learning Approach

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also su...

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Bibliographic Details
Published in:IEEE transactions on very large scale integration (VLSI) systems 2020-12, Vol.28 (12), p.2584-2597
Main Authors: Datta, Piyali, Chakraborty, Arpan, Pal, Rajat Kumar
Format: Article
Language:English
Subjects:
Automation
Biochips
Co-design
codesign
Congestion
Electrodes
Inkjet printing
Low cost
Machine learning
Microfluidics
paper-based biochips
Prediction models
Predictive control
Predictive models
Printers (data processing)
regression
Routing
Schedules
synthesis
Wiring
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Summary:Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.
ISSN:1063-8210
1557-9999
DOI:10.1109/TVLSI.2020.3030501