Efficient parallel testing and diagnosis of digital microfluidic biochips

Abstract

Microfluidics-based biochips consist of microfluidic arrays on rigid substrates through which movement of fluids is tightly controlled to facilitate biological reactions. Biochips are soon expected to revolutionize biosensing, clinical diagnostics, environmental monitoring, and drug discovery. Critical to the deployment of the biochips in such diverse areas is the dependability of these systems. Thus robust testing and diagnosis techniques are required to ensure adequate level of system dependability. Due to the underlying mixed technology and mixed energy domains, such biochips exhibit unique failure mechanisms and defects. In this article efficient parallel testing and diagnosis algorithms are presented that can detect and locate single as well as multiple faults in a microfluidic array without flooding the array, a problem that has hampered realistic implementation of several existing strategies. The fault diagnosis algorithms are well suited for built-in self-test that could drastically reduce the operating cost of microfluidic biochip. Also, the proposed alogirthms can be used both for testing and fault diagnosis during field operation as well as increasing yield during the manufacturing phase of the biochip. Furthermore, these algorithms can be applied to both online and offline testing and diagnosis. Analytical results suggest that these strategies that can be used to design highly dependable biochip systems.