Adaptive error recovery in MEDA biochips based on droplet-aliquot operations and predictive analysis

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; 3) the need for special fabrication processes. To overcome these problems, DMFBs based on micro-electrode-dot-array (MEDA) have recently be-en 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. Experimental results on three representative benchmarks demonstrate the efficiency of the proposed error-recovery strategy.