ABSTRACT
Advances in droplet-based "digital" microfluidics have led to the emergence of biochip devices for automating laboratory procedures in biochemistry and molecular biology. These devices enable the precise control of nanoliter-volume droplets of biochemical samples and reagents. Therefore, integrated circuit (IC) technology can be used to transport and transport "chemical payload" in the form of micro/nanofluidic droplets. As a result, non-traditional biomedical applications and markets (e.g., high-throughout DNA sequencing, portable and point-of-care clinical diagnostics, protein crystallization for drug discovery), and fundamentally new uses are opening up for ICs and systems.
However, continued growth depends on advances in chip integration and design-automation tools. Design automation is needed to ensure that biochips are as versatile as the macro-labs that they are intended to replace, and researchers can thereby envision an automated design flow for biochips, in the same way as design automation revolutionized IC design in the 80s and 90s.
This talk will first provide an overview of market drivers such as immunoassays, DNA sequencing, clinical chemistry, etc., and electrowetting-based digital microfludic biochips. The audience will next learn about design automation, design-for-testability, and reconfiguration aspects of digital microfluidic biochips. Synthesis tools will be described to map assay protocols from the lab bench to a droplet-based microfluidic platform and generate an optimized schedule of bioassay operations, the binding of assay operations to functional units, and the layout and droplet-flow paths for the biochip. The role of the digital microfluidic platform as a "programmable and reconfigurable processor" for biochemical applications will be highlighted. Finally, the speaker will demonstrate dynamic adaptation of bioassays through cyberphysical system integration and sensor-driven on-chip error recovery.
Index Terms
- Digital Microfluidic Biochips: Towards Functional Diversity, More than Moore, and Cyberphysical Integration
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