Microfluidic-based biochips are replacing the conventional biochemical analyzers, and are able to integrate on-chip all the necessary functions for biochemical analysis using microfluidics. The digital microfluidic biochips are based on the manipulation of liquids not as a continuous flow, but as discrete droplets. Researchers have presented approaches for the synthesis of digital microfluidic biochips, which, starting from a biochemical application and a given biochip architecture, determine the allocation, resource binding, scheduling and placement of the operations in the application. Existing approaches consider that on-chip operations, such as splitting a droplet of liquid, are perfect. However, these operations have variability margins, which can impact the correctness of the biochemical application.We consider that a split operation, which goes beyond specified variability bounds, is faulty. The fault is detected using on-chip volume sensors. We have proposed an abstract model for a biochemical application, consisting of a sequencing graph, which can capture all the fault scenarios in the application. Starting from this model, we have proposed a synthesis approach that, for a given chip area and number of sensors, can derive a fault-tolerant implementation. Two fault-tolerant scheduling techniques have been proposed and compared. We show that, by taking into account fault-occurrence information, we can derive better quality implementations, which leads to shorter application completion times, even in the case of faults. The proposed synthesis approach under operation variability has been evaluated using several benchmarks.
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