Design, Optimization, and Test Methods for Micro-Electrode-Dot-Array Digital Microfluidic Biochips
Digital microfluidic biochips (DMFBs) are revolutionizing many biochemical analysis procedures, e.g., high-throughput DNA sequencing and point-of-care clinical diagnosis. However, today's DMFBs suffer from several limitations: (1) constraints on droplet size and the inability to vary droplet volume in a fine-grained manner; (2) the lack of integrated sensors for real-time detection; (3) the need for special fabrication processes and the associated reliability/yield concerns.
To overcome the above limitations, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have recently been proposed. Unlike conventional digital microfluidics, where electrodes of equal size are arranged in a regular pattern, the MEDA architecture is based on the concept of a sea-of-micro-electrodes. The MEDA architecture allows microelectrodes to be dynamically grouped to form a micro-component that can perform different microfluidic operations on the chip.
Design-automation tools can reduce the difficulty of MEDA biochip design and help to ensure that the manufactured biochips are versatile and reliable. In order to fully exploit MEDA-specific advantages (e.g., real-time droplet sensing), new design, optimization, and test problems are tackled in this dissertation.
The dissertation first presents a droplet-size aware synthesis approach that can configure the target bioassay on a MEDA biochip. The proposed synthesis method targets reservoir placement, operation scheduling, module placement, and routing of droplets of various sizes. An analytical model for droplet velocity is proposed and experimentally validated using a fabricated MEDA chip.
Next, this dissertation presents an efficient error-recovery strategy to ensure the correctness of assays executed on MEDA biochips. By exploiting MEDA-specific advances in droplet sensing, the dissertation presents a novel probabilistic timed automata (PTA)-based error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors. An on-line synthesis technique and a control flow are also proposed to connect local-recovery procedures with global error recovery for the complete bioassay.
A potentially important application of MEDA biochips lies in sample preparation via a series of dilution steps. Sample preparation in digital microfluidic biochips refers to the generation of droplets with target concentrations for on-chip biochemical applications. The dissertation presents the first droplet size-aware and error-correcting sample-preparation method for MEDA biochips. In contrast to previous methods, the proposed approach considers droplet sizes and incorporates various mixing models in sample preparation.
In order to ensure high confidence in the outcome of biochemical experiments, MEDA biochips must be adequately tested before they can be used for bioassay execution. The dissertation presents efficient structural and functional test techniques for MEDA biochips. The proposed structural test techniques can effectively detect defects and identify faulty microcells, and the proposed functional test techniques address fundamental fluidic operations on MEDA biochips.
In summary, the dissertation tackles important problems related to key stages of MEDA chip design and usage. The results emerging from this dissertation provide the first set of comprehensive design-automation solutions for MEDA biochips. It is anticipated that MEDA chip users will also benefit from these optimization methods.
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Rights for Collection: Duke Dissertations