Rapid online fault recovery for cyber-physical digital microfluidic biochips

Abstract

Abstract-Microfluidic technologies offer benefits to the biological sciences by miniaturizing and automating chemical reactions. Software-controlled laboratories-on-a-chip (LoCs) execute biological protocols (assays) specified using high-level languages. Integrated sensors and video monitoring provide a closed feedback loop between the LoC and its control software, which provide timely information about the progress of an ongoing assay and the overall health of the LoC. This paper introduces a cyber-physical control algorithm that rectifies hard and soft faults that are detected dynamically while executing an assay on a digital microfluidic biochip (DMFB), one specific LoC technology. The approach is scalable (i.e., there is no fixed limit on the number of faults that may occur), and runs efficiently in practice, thereby limiting the performance overhead incurred when a hard or soft fault occurs during assay execution.

Puddle

“…An online solution has also been presented [4], but it has an offline component and only works for programs represented as DAGs. Previous work has also proposed error correction for DMF devices [25,26,28,59,60], but these were implemented outside of a larger microfluidic programming solution, relied on more expensive sensors, or were only simulated.…”

Synthesis of a Cyberphysical Hybrid Microfluidic Platform for Single-Cell Analysis

“…The steps used for generating the nodes and the associated edges (Lines 15-24) are similar to the previous part. Finally, the algorithm generates the output nodes and the edges that connect them to the previous level (Lines [26][27][28][29][30][31].…”

BioScript: programming safe chemistry on laboratories-on-a-chip

“…The ability to perform sensing, computation, and actuation based on the results of the computation adds control flow to the instruction set of the DMFB. Prior work has applied feedback-control for precise droplet positioning [Alistar and Gaudenz 2017;Basu 2013;Bhattacharjee and Najjaran 2012;Hu et al 2013;Li et al 2015Li et al , 2017Murran and Najjaran 2012;Shih et al 2011;Vo et al 2017] and online error detection and recovery [Alistar and Pop 2015;Alistar et al 2016;Hsieh et al 2014;Ibrahim and Chakrabarty 2015a,b;Ibrahim et al 2017;Jaress et al 2015;Li et al 2017;Luo et al 2013a,b;Poddar et al 2016;Zhao et al 2010]; efforts to leverage these capabilities to provide control flow constructs at the language syntax level have been far more limited [Curtis and Brisk 2015;Curtis et al 2018;.…”

“…If the BioScript program is typeable, the compiler passes the SSI-based CFG to the execution engine, which performs code generation (scheduling, placement, and routing), which may be performed either statically [Curtis et al 2018] or dynamically . The execution engine processes sensory feedback produced by the DMFB, including dynamic error detection and recovery; it may be necessary to re-compile parts of the assay, especially if a hard fault has been detected, rendering a portion of the device unusable; prior work has covered dynamic error recovery in detail [Alistar and Pop 2015;Alistar et al 2016;Hsieh et al 2014;Ibrahim and Chakrabarty 2015a,b;Ibrahim et al 2017;Jaress et al 2015;Li et al 2017;Luo et al 2013a,b;Poddar et al 2016;Zhao et al 2010]. The execution engine terminates successfully when the control flow reaches the CFG exit node or unsuccessfully if the error recovery mechanism fails for any reason.…”