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Author Datta, Siddhartha ♦ Joshi, Bharat ♦ Ravindran, Arun ♦ Mukherjee, Arindam
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2009
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Microfluidic biochip ♦ Defect tolerance ♦ Droplet flooding ♦ Fault tolerance ♦ Microfluidics ♦ Multiple faults ♦ Reconfigurability ♦ Testing
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.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2009-07-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 5
Issue Number 2
Page Count 17
Starting Page 1
Ending Page 17

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Source: ACM Digital Library