Abstract
Digital microfluidic biochips (DMFBs) are being increasingly used in biochemistry labs for automating bioassays. However, traditional DMFBs suffer from some key shortcomings: 1) inability to vary droplet volume in a flexible manner; 2) difficulty of integrating on-chip sensors; and 3) the need for special fabrication processes. To overcome these problems, DMFBs based on micro-electrode-dot -array (MEDA) have recently been proposed. However, errors are likely to occur on a MEDA DMFB due to chip defects and the unpredictability inherent to biochemical experiments. We present fine-grained error-recovery solutions for MEDA by exploiting real-time sensing and advanced MEDA-specific droplet operations. The proposed methods rely on adaptive droplet-aliquot operations and predictive analysis of mixing. In addition, a roll-forward error-recovery method is proposed to efficiently utilize the unused part of the biochip and reduce the time required for error recovery. Experimental results on three representative benchmarks demonstrate the efficiency of the proposed error-recovery strategy.
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