Digital Microfluidic Biochips: Towards Hardware/Software Co-Design and Cyberphysical System IntegrationAdvances in droplet-based digital microfluidic biochips (DMFBs) have led to the emergence of biochips for automating laboratory procedures in biochemistry and molecular biology. These devices enable the precise control of microliter of nanoliter volumes of biochemical samples and reagents. They combine electronics with biology, and integrate various bioassay operations, such as sample preparation, analysis, separation, and detection. To meet the challenges of increasing design complexity and precision, the interplay between hardware and software through sensor-based cyberphysical integration will be involved to build DMFBs effectively. This talk offers attendees an opportunity to bridge the semiconductor ICs/system industry with the biomedical and pharmaceutical industries. The talk will first describe emerging applications in biology and biochemistry that can benefit from advances in electronic "biochips". The presenter will next describe technology platforms for accomplishing "biochemistry on a chip", and introduce the audience to microarrays and fluidic actuation methods based on microfluidics. The droplet-based "digital" microfluidic platform based on electrowetting will be described in considerable detail. Next, the presenter will describe fabrication techniques for digital microfluidic biochips, followed by computer-aided design, design-for-testability, cyberphysical integration, and reconfiguration aspects of chip/system design. Synthesis algorithms and methods will be presented to map behavioral descriptions to a digital microfluidic platform, and generate an optimized schedule of bioassay operations, chip layout, and droplet-flow paths. In this way, the audience will see how a "biochip compiler" can translate protocol descriptions provided by an end user (e.g., a chemist or a nurse at a doctor's clinic) to a set of optimized and executable fluidic instructions that will run on the underlying digital microfluidic platform.
Top-Down Synthesis for Flow-Based Microfluidic Biochips
As the design complexity rapidly increases, the manufacture and the biochemical analysis of flow-based microfluidic biochip become more complicated. According to recent study, the biochips can now use more than 25,000 valves and about a million features to run 9,216 parallel polymerase chain reactions. Moreover, the number of mechanical valves per square inch for flow-based microfluidic biochips has grown exponentially and four times faster than the reflection of Moore's Law. Although the scale for flow-based microfluidic biochips is enlarging and the total amount of the valves fabricated on a chip are also growing significantly, computer-aided design (CAD) tools are still in their infancy today. Designers are using bottom-up full-custom design approaches involving multiple non-automated steps to manually adjust the components and the connection to satisfy the steps of desired biochemical applications. As a result, the development of explicit design rules and strategies allowing modular top-down synthesis methodologies are needed, in order to provide the same level of CAD support for the biochip designer as the one that are currently done for the semiconductor industry. However, for miniaturization, integration, automation and parallelization of biochemical processes, a flow-based microfluidic biochip needs a lot of chip-integrated micro-valves, i.e. the basic unit of fluid-handling functionality, to manipulate the fluid flow for biochemical applications. Moreover, frequent switching of micro-valves may cause power consumption and even reliability problems. To minimize the valve-switching activities, this talk presents a top-down synthesis methodology based on breadth first search (BFS) and minimum cost maximum flow (MCMF) to synthesize the flow-based microfluidic biochip. The experimental results show that our methodology not only makes significant reduction of valve-switching activities but also diminishes the application completion time for both real-life applications and a set of synthetic benchmarks.