With the evolution of the technology, digital microfluidic (DMF) biochips have become a vital part of biochemical research. Hence, it is required to consider the reliability of the different fluidic operations performed on a biochip. Sample preparation is an important process of any real-life bioprotocol implementation on a DMF biochip. In this process, sequence of mixing and dilution steps are determined to get the desired target concentrations. The mixers used for performing mix-split steps may incorporate some noise during mixing and can result in the erroneous concentrations of the reagents. Thus, the reliability analysis of the resultant target concentration is necessary and methods are required to be developed to reduce these concentration errors. In this paper, the error and reliability models are discussed to compare the reliabilities of the existing mixing algorithms. Simulation results show that for a given target ratio, reliability of common dilution operation sharing (Liuet al., ICCAD-2013) is higher. We also discuss the mixer assignment techniques and the heuristic approach is found to quickly provide the better order of mixer assignment in order to achieve highly reliable mixture after sample preparation.

In recent years, digital microfluidic biochips are being dominantly used for implementing a wide range of biochemical laboratory protocols (bioprotocols) on hand-held devices. Accurate preparation of fluid-samples is a fundamental preprocessing step that is needed in many bioprotocols. Oftentimes, the number of reservoirs built on-chip may be far less than that of the reactant fluids to be mixed. Hence, during the execution of an assay, several fluids are to be unloaded from the reservoirs to make room for loading new fluids stored off-line. Such unload-wash-load steps (switching) may be required several times, and these steps, being manual, significantly impact assay-completion time. In this paper, we propose a new scheduling scheme namely Reservoir and Mixer constrained Scheduling (RMS) that can schedule a mixing tree obtained by a mixing algorithm, while minimizing the number of switching such that the total completion time can be minimized. Simulation results over a large number of target ratios show that given the mixing trees obtained by standard mixing algorithms such as MinMix/RMA/CoDOS, RMS reduces switching steps (on average by 40.3%/41.9%/33%) at the cost of increasing mixing time (by only 3.5%/6.2%/4.8%), compared to an existing scheduling scheme invoked with reservoir constraints.