Proceedings ArticleDOI
Error recovery in a micro-electrode-dot-array digital microfluidic biochip?
Zipeng Li,Kelvin Yi-Tse Lai,Po-Hsien Yu,Krishnendu Chakrabarty,Miroslav Pajic,Tsung-Yi Ho,Chen-Yi Lee +6 more
- pp 105
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TLDR
By exploiting MEDA-specific advances in droplet sensing, this work presents a novel error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors.Abstract:
A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. However, today's DMFBs suffer from several limitations: (i) constraints on droplet size and the inability to vary droplet volume in a fine-grained manner; (ii) the lack of integrated sensors for real-time detection; (iii) the need for special fabrication processes and the associated reliability/yield concerns. To overcome the above problems, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have been proposed recently, and droplet manipulation on these devices has been experimentally demonstrated. Errors are likely to occur due to defects, chip degradation, and the lack of precision inherent in biochemical experiments. Therefore, an efficient error-recovery strategy is essential to ensure the correctness of assays executed on MEDA biochips. By exploiting MEDA-specific advances in droplet sensing, we present a novel error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors. Local recovery strategies based on probabilistic-timed-automata are presented for various types of errors. A control flow is also proposed to connect local recovery procedures with global error recovery for the complete bioassay. Laboratory experiments using a fabricated MEDA chip are used to characterize the outcomes of key droplet operations. The PRISM model checker and three analytical chemistry benchmarks are used for an extensive set of simulations. Our results highlight the effectiveness of the proposed error-recovery strategy.read more
Citations
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Journal ArticleDOI
Droplet Size-Aware High-Level Synthesis for Micro-Electrode-Dot-Array Digital Microfluidic Biochips
TL;DR: This work presents the first synthesis approach that can be used for MEDA biochips and presents the proposed synthesis method targeting reservoir placement, operation scheduling, module placement, routing of droplets of various sizes, and diagonal movement ofdroplets in a two-dimensional array.
Journal ArticleDOI
Droplet Size-Aware and Error-Correcting Sample Preparation Using Micro-Electrode-Dot-Array Digital Microfluidic Biochips
TL;DR: This work adopts a next generation DMFB platform, referred to as micro-electrode-dot-array (MEDA), for sample preparation, and proposes the first sample-preparation method that exploits the MEDA-specific advantages of fine-grained control of droplet sizes and real-time droplet sensing.
Journal ArticleDOI
Adaptive and Roll-Forward Error Recovery in MEDA Biochips Based on Droplet-Aliquot Operations and Predictive Analysis
TL;DR: Fine-grained error-recovery solutions for MEDA are presented by exploiting real-time sensing and advanced MEDA-specific droplet operations and predictive analysis of mixing and rely on adaptive droplet-aliquot operations and predictability of mixing.
Proceedings ArticleDOI
Built-in self-test for micro-electrode-dot-array digital microfluidic biochips
TL;DR: The proposed BIST architecture can effectively detect defects in a MEDA biochip, and faulty microcells can be identified, and simulation results based on HSPICE and experiments using fabricated MEDABiochips highlight the effectiveness of the proposed Bist architecture.
Journal ArticleDOI
Efficient and Adaptive Error Recovery in a Micro-Electrode-Dot-Array Digital Microfluidic Biochip
Zipeng Li,Kelvin Yi-Tse Lai,John McCrone,Po-Hsien Yu,Krishnendu Chakrabarty,Miroslav Pajic,Tsung-Yi Ho,Chen-Yi Lee +7 more
TL;DR: A novel error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors and an integer linear programming-based method to select the optimal local- recovery time for each operation is presented.
References
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