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A digital microfluidic biochip (DMFB) enables the miniaturization of immunoassays, point-of-care clinical diagnostics, and DNA sequencing. A recent generation of DMFBs uses a microelectrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and real-time droplet sensing using CMOS technology. However, microelectrodes in a MEDA biochip can degrade due to charge trapping when they are repeatedly charged and discharged during bioassay execution; such degradation leads to the failure of microelectrodes and erroneous bioassay outcomes. To address this problem, we first introduce a new microelectrode-cell design such that we can obtain the health status of all the microelectrodes in a MEDA biochip by employing the inherent sensing mechanism. Next, we present a stochastic game-based model for droplet manipulation, and a formal synthesis method for droplet routing that can dynamically change droplet transportation routes. This adaptation is based on the real-time health information obtained from microelectrodes. Comprehensive simulation results for four real-life bioassays show that our method increases the likelihood of successful bioassay completion with negligible impact on time-to-results. [ABSTRACT FROM AUTHOR]

A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. In recent years, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have been proposed. MEDA biochips can provide advantages of better capability of droplet manipulation and real-time sensing ability. However, errors are likely to occur due to defects, chip degradation, and the lack of precision inherent in biochemical experiments. Therefore, an efficient error-recovery strategy is essential to ensure the correctness of assays executed on MEDA biochips. By exploiting MEDA-specific advances in droplet sensing, we present a novel error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors. Local recovery strategies based on probabilistic-timed-automata are presented for various types of errors. An online synthesis technique and a control flow are also proposed to connect local-recovery procedures with global error recovery for the complete bioassay. Moreover, an integer linear programming-based method is also proposed to select the optimal local-recovery time for each operation. Laboratory experiments using a fabricated MEDA chip are used to characterize the outcomes of key droplet operations. The PRISM model checker and three benchmarks are used for an extensive set of simulations. Our results highlight the effectiveness of the proposed error-recovery strategy. [ABSTRACT FROM PUBLISHER]