Digital microfluidic biochips: synthesis, testing, and by Krishnendu Chakrabarty

By Krishnendu Chakrabarty

Digital Microfluidic Biochips makes a speciality of the automatic layout and creation of microfluidic-based biochips for large-scale bioassays and safety-critical functions. Bridging components of digital layout automation with microfluidic biochip learn, the authors current a system-level layout automation framework that addresses key matters within the layout, research, and trying out of electronic microfluidic biochips.

The booklet describes a brand new iteration of microfluidic biochips with extra complicated designs that supply dynamic reconfigurability, approach scalability, method integration, and illness tolerance. half I describes a unified layout method that pursuits layout optimization below source constraints. half II investigates within your means checking out strategies for electronic microfluidic biochips that come with try source optimization and fault detection whereas operating common bioassays. half III makes a speciality of diverse reconfiguration-based disorder tolerance strategies designed to extend the yield and dependability of electronic microfluidic biochips.

Expanding upon effects from ongoing learn on CAD for biochips at Duke college, this booklet offers new layout methodologies that tackle the various obstacles in present full-custom layout techniques. electronic Microfluidic Biochips is a necessary source for reaching the combination of microfluidic parts within the subsequent iteration of system-on-chip and system-in-package designs.

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Upper Bound (UB): Consider separating the operations into three sequencing phases. In each phase, only one type of operation (Input, Mixing, or Detection) can be performed. 5(NMix1 + NMix2 + … + NMixi-1)), where i > 1. 18 Derivation of (a) lower and (b) upper bounds. 18(b). The schedule obtained from this three-phase approach is a feasible solution, but the corresponding completion time is an upper bound on the optimum completion time. 1 EVALUATION EXPERIMENTS Five examples are used to evaluate the heuristics described previously.

The organization of the remainder of the chapter is as follows. 2 discusses related prior work. 3, we present a simulated annealing-based heuristic for module placement in dynamically reconfigurable biochips. 4, the reconfiguration technique is studied in more detail, and the fault tolerance index (FTI) for single faults is defined. A fast algorithm to determine the FTI value is also presented. fm Page 42 Monday, July 24, 2006 2:59 PM 42 Digital Microfluidic Biochips be extended to handle multiple faults.

We assume Nr = Nd = 1, and Na = 4. We use a popular public domain ILP solver called lpsolve [59]. 12. , 34 sec). 4 HEURISTICS FOR THE SCHEDULING PROBLEM The scheduling problem being studied here is equivalent to the resource-constrained scheduling problem with nonuniform weights of operation nodes, which has been proven to be NP-complete [60,61]. In order to solve this problem in a computationally efficient manner for large instances, we develop heuristics in this section. • Modified List Scheduling Algorithm.

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