Forecast-Based Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs

Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, m...

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Bibliographic Details
Published in:Proceedings - IEEE International Conference on Computer Design pp. 422 - 428
Main Authors: Song, Ling-Yen, Chen, Yi-Ling, Lei, Yung-Chun, Huang, Juinn-Dar
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2019
Subjects:
correction on demand
digital microfluidic biochip
Digital microfluidic biochips
Electrodes
forecast-based correction
Hardware
Optimization
probability-based forecast
Reservoirs
sample preparation
Solid modeling
System-on-chip
unbalanced splitting
ISSN:2576-6996
Online Access:Get full text
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Summary:Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, most of previous works assumed that mixing-then-splitting would get two identical output droplets, which is not always true due to unbalanced splitting. As a consequence, those works may fail to provide correct solutions at the presence of unbalanced splitting. Several methods have been proposed to deal with this issue. Nevertheless, some of them rely on hypotheses that may not be practical, while the others demand extra reactants or special hardware. In this paper, we propose a new probability-based sample preparation algorithm for unbalanced splitting correction. Our new algorithm not only guarantees a correct solution, but requires neither extra reactants nor on-chip special hardware. Experimental results show that the effect of unbalanced splitting can be eliminated only at the cost of 20% more operation steps. That is, the proposed algorithm is both reliable and efficient.
ISSN:2576-6996
DOI:10.1109/ICCD46524.2019.00066