Component Placement Algorithm Considering Reagent Type Differences in Cell Reuse for FPVA Biochips

ObjectiveFully Programmable Valve Array (FPVA) biochips, a recent type of flow-based microfluidic biochip, offer high flexibility and programmability, which enables them to meet different and complex experimental needs. Component placement is a critical stage in FPVA architectural synthesis because it affects several performance metrics, including assay completion time, total fluid-transport length, and cross-contamination. Cell reuse, an essential feature of FPVA programmability, requires special consideration during placement. However, existing studies have largely ignored the effect of reagent type differences in cell reuse on these metricsMethodsThis study presents a component placement algorithm for FPVA biochips that accounts for reagent type differences during cell reuse. The algorithm first introduces a cell reuse complexity metric that quantifies reuse complexity by considering the effects of reagent-type differences and component overlap on cross-contamination. It then integrates constraints, including placement-area limits and non-overlapping conditions for concurrent components, to ensure valid placement. The reward function is optimized to minimize reuse complexity and reduce the distance between components that use the same reagent type. The goal is to lower cross-contamination, total fluid-transport length, and assay completion time.Results and DiscussionsThe algorithm is evaluated on benchmark FPVA instances with different chip sizes and functional requirements and compared with related methods. It reduces cell reuse complexity by 34.2%, assay completion time by 2.8%, and total fluid-transport length by 9.2% on average (Table 2). It also reduces the reagent-aware distance metric by 29.9% on average (Fig. 6). The learning agent’s decision trajectories show clear spatial structure, which reflects global placement awareness.ConclusionsThis study is the first to investigate FPVA component placement with attention to reagent type differences in cell reuse. The main contributions are as follows: (1) a cell reuse complexity metric is proposed to assess reuse intensity in placement, (2) the FPVA placement problem is modeled as a Markov decision process to enable the use of double deep Q-networks for safe and efficient placement policy learning, and (3) compared with existing work, the model improves FPVA biochemical assay performance and reliability.