Being a structure like a two-dimensional (2D) array of microvalves and cells, Programmable Microfluidic Device PMD biochips have the characteristics of reconfigurability and flexibility unlike conventional flow-based microfluidic biochips. In recent years, several design automation techniques for PMD biochips have been reported. For automated control of the PMD chip implementing a bioprotocol, one of the important tasks is to minimize the number of fluid flows for loading the reactant fluids into specific cells before the bioprotocol is executed. In this work we intensively study the fluid loading problem for PMD chips and we propose a two-phase approach to solve this problem. First, we propose a constraint satisfaction problem (CSP) based method, called loading-aware fluid-to-cell assignment (LAFCA) in order to obtain a suitable fluid-to-cell assignment, which will be beneficial for fluid loading phase. Then we propose an exact method, called CSP-based loading algorithm (CSPLA) and a near-optimal heuristic method, called determining flows from the last (DFL), for determining a sequence of fluid flows required to load different fluids into the cells of a PMD chip. We formulate CSPLA as a single objective optimization problem to minimize the total number of flows. For the output as a sequence of fluid flows we define three loading parameters, the total number of flows (K), the total number of 90° bends in all flow paths (B), and the total flow path length (L). Simulation results confirm that LAFCA combined with CSPLA outperforms (K, B and L reduced by 63.9%, 31.7% and 59.9%, respectively) the state-of-the-art method fluid loading algorithm for PMD (FLAP) [Gupta et al., TODAES, 2019]. Whereas, LAFCA combined with DFL can reduce the loading parameters K, B and L by 61.2%, 20.8% and 57.4%, respectively over using only FLAP. Also from the overall simulation results we can conclude that for many testcases, DFL can find the near-optimal Ks.

可编程微流控设备PMD生物芯片具有微阀和细胞的二维（2D）阵列结构，与传统的基于流的微流体生物芯片不同，具有可重构性和灵活性。近年来，已经报道了几种用于PMD生物芯片的设计自动化技术。为了自动控制实施生物协议的PMD芯片，重要任务之一是在执行生物协议之前，最小化用于将反应物流体加载到特定细胞中的流体流量。在这项工作中，我们深入研究了PMD芯片的流体加载问题，并提出了两阶段方法来解决该问题。首先，我们提出一种基于约束满足问题（CSP）的方法，称为加载感知的流体到单元分配（LAFCA），以获得合适的流体到单元的分配，这对于流体加载阶段将是有益的。然后，我们提出了一种精确的方法，称为基于CSP的加载算法（CSPLA）和一种接近最优的启发式方法，称为从末尾确定流量（DFL），用于确定将不同的流体加载到单元格中所需的流体流序列。 PMD芯片。我们将CSPLA公式化为单个目标优化问题，以最大程度地减少流程总数。对于作为流体流序列的输出，我们定义了三个加载参数，即流总数（K），所有流道（B）的弯曲总数为90° ，总流道长度（L）。仿真结果证实，LAFCA联合CSPLA性能优于（ķ，乙和大号由63.9％降低，分别为31.7％和59.9％，）的状态的最先进的方法用于PMD流体装载算法（FLAP）[Gupta等人。 ，TODAES，2019年]。相比于仅使用FLAP，LAFCA与DFL的组合可将载荷参数K，B和L分别降低61.2％，20.8％和57.4％。从整体仿真结果还可以得出结论，对于许多测试案例，DFL可以找到接近最优的K s。