Placement of defect-tolerant digital microfluidic biochips using the T-tree formulation

Abstract

Droplet-based microfluidic biochips have recently gained much attention and are expected to revolutionize the biological laboratory procedures. As biochips are adopted for the complex procedures in molecular biology, its complexity is expected to increase due to the need of multiple and concurrent assays on a chip. In this article, we formulate the placement problem of digital microfluidic biochips with a tree-based topological representation, called T-tree. To the best knowledge of the authors, this is the first work that adopts a topological representation to solve the placement problem of digital microfluidic biochips. We also consider the defect tolerant issue to avoid to use defective cells due to fabrication. Experimental results demonstrate that our approach is more efficient and effective than the previous unified synthesis and placement framework.

Fast Online Synthesis of Digital Microfluidic Biochips

“…Several papers, however, solve some of these problems together, using iterative improvement heuristics [15], [27], [28], [33], [35], whose runtime is prohibitive. These approaches address problems that can arise when one stage of synthesis does not consider the next.…”

“…With our topology in mind, we present several constraints are necessary and apply them to list scheduling [26] and path scheduling [11] to quickly produce schedules. Placement, which has been solved in the past by iterative improvement algorithms [27], [35] or integer linear programming (ILP) [14], is simplified to a binding problem, which can be solved efficiently in polynomial-time. We present two fast binding solutions.…”

“…The objective for most placers is to pack as many concurrent operations/modules into as little area as possible. Several direct-addressing placement and unified scheduling-placement algorithms [27], [28], [33], [35] use simulated annealing, which run in minutes or tens of seconds; in contrast, our online flow completes in tens of milliseconds.…”

Force-Directed List Scheduling for Digital Microfluidic Biochips

“…Yu et al [14] combine scheduling and placement into a 3D placement problem in space and time; they solve the problem using simulated annealing and a data structure (the T-tree) to represent the placement. Maftei et al [6] solve scheduling, module selection, placement, and routing using Tabu Search.…”

Design Automation and Test Solutions for Digital Microfluidic Biochips

“…Recent years have seen growing interest in the automated design and synthesis of microfluidic biochips [39], [44], [47]- [51], [53]- [56], [59]- [61], [63]- [67], [69]. Optimization goals here include the minimization of assay completion time, minimization of chip area, and higher defect tolerance.…”