Rapid growth in capacity makes flow-based microfluidic biochips a promising candidate for biochemical analysis because they can integrate more complex functions. However, as the number of components grows, the total length of flow channels between components must increase exponentially. Recent empirical studies show that long flow channels are vulnerable due to blocking and leakage defects. Thus, it is desirable to minimize the total length of flow channels for robustness. Also, for timing-sensitive biochemical assays, increase in the longest length of flow channel will delay the assay completion time and lead to variation of fluid, thereby affecting the correctness of outcome. The increasing number of components, including the pre-placed components, on the chip makes the flow channel routing problem even more complicated. In this paper, we propose an efficient obstacleavoiding rectilinear Steiner minimum tree algorithm to deal with flow channel routing problem in flow-based microfluidic biochips. Based on the concept of Kruskal algorithm and formulating the considerations as a bi-criteria function, our algorithm is capable of simultaneously minimizing the total length and the longest length of flow channel.
We consider the multi-layer obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem and propose a reduction to transform a multi-layer instance into a 3D instance. Based on the reduction we apply computational geometry techniques to develop an efficient algorithm, utilizing existing OARSMT heuristics. Experimental results show that our algorithm provides a solution with excellent quality and has a significant speed-up compared to previously known results.
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