Capacity-Aware Wash Optimization with Dynamic Fluid Scheduling and Channel Storage for Continuous-Flow Microfluidic Biochips

Continuous-flow microfluidic biochips are gaining increasing attention with promising applications for automatically executing various laboratory procedures in biology and biochemistry. Biochips with distributed channel-storage architectures enable each channel to switch between the roles of transportation and storage. Consequently, fluid transportation, caching, and fetch can occur concurrently through different flow paths. When two dissimilar types of fluidic flows occur through the same channels in a time-interleaved manner, it may cause contamination to the latter as some residues of the former flow may be stuck at the channel wall during transportation. To remove the residues, wash operations are introduced as an essential step to avoid incorrect assay outcomes. However, existing work has been considered that the washing capacity of a buffer fluid is unlimited. In the actual scenario, a fixed-volume buffer fluid irrefutably possesses a limited washing capacity, which can be successively consumed while washing away residues from the channels. Hence, capacity-aware wash scheme is a basic requirement to fulfil the dynamic fluid scheduling and channel storage. In this paper, we formulate a practical wash optimization problem for microfluidic biochips, which considers the requirements of dynamic fluid scheduling, channel storage, as well as washing capacity constraints of buffer fluids simultaneously, and present an efficient design flow to solve this problem systematically. Given the high-level synthesis result of a biochemical application and the corresponding component placement solution, our goal is to complete a contamination-aware flow-path planning with short flow-channel length. Meanwhile, the biochemical application can be executed efficiently and correctly with an optimized capacity-aware wash scheme. Experimental results show that compared to a state-of-the-art washing method, the proposed method achieves an average reduction of 26.1%, 43.1%, and 34.1% across all the benchmarks with respect to the total channel length, total wash time, and execution time of bioassays, respectively.