Multilayered continuous-flow microfluidic biochips are rapidly advancing platforms for delicate bio-applications. The high complexity of biochip structures and application protocols drives the growing demand for design automation solutions. Current research enables the automatic synthesis of the physical layout and the scheduling and binding protocols of biochips, showcasing the significant potential of microfluidic design automation for improved resource utilization and reduced bioassay completion time. However, state-of-the-art synthesis methods primarily focus on device and operation levels, assuming flow paths are always available and neglecting interactions of flow and control channels. This creates a critical gap in the synthesis process, causing performance degradation, resource redundancy, or even infeasible designs. This work bridges this gap with a two-stage approach. Firstly, we perform a mathematical model to synthesize a high-level protocol that specifies the paths and execution orders of fluid transportation operations. Specifically, we construct flow paths based on the fluidic architecture of a given biochip design and optimize scheduling schemes to minimize the completion time of a given bioassay. Next, we perform a simulation-based synthesis of control channel pressurization sequences to realize the high-level protocol. Experimental results confirm that the proposed approach efficiently validates flow paths for feasible designs, identifies conflicting design features in infeasible designs, and improves the design efficiency and quality: compared to the original designs, it reduces the average number of control channels by 49%, and compared to the preliminary work, it reduces the average fluid transportation time by 19% and the average program run time by 38%.

Inkjet-printed electronics is a low-cost option for large-scale production. To avoid manufacturing defects, recent research has considered design constraints, such as Laplace and proximity conflicts, decomposed the layouts into different layers, and printed them sequentially. The state-of-the-art work reduced the manufacturing time by optimizing the number of layers and drying time. In this work, we aim to enhance manufacturing efficiency from a new angle, concurrently optimizing the printing time and the layout decomposition of inkjet-printed electronics. We propose an integer linear programming formulation and a dynamic programming algorithm to determine layout decomposition and layer assignment and to estimate the total printing time by carefully considering printing characteristics and design constraints. Experimental results demonstrate significant reductions in overall printing time, leading to improved fabrication efficiency.

Multilayered continuous-flow microfluidic biochips are highly valued for their miniaturization and high bio-application throughput. However, challenges arise as the dynamic connections of channels, adjusted to satisfy varying demands of fluid transportation at different moments, complicate the execution of bioassays. The existing methods often focus on device binding and operation scheduling during high-level synthesis but overlook the topological connections within the microfluidic network. This oversight leads to mismanagement of conflicts between fluid transportations and erroneous assumptions about constant flow velocities, resulting in decreased accuracy and efficiency or even infeasibility of bioassay execution. To address this problem, we mathematically model the flow velocity that varies according to the dynamic changes of the topological connections between the on-chip components during the execution of the bioassay. Further integrating the flow velocity model into the high-level synthesis, we propose a quadratic programming (QP) method that constructs flow paths and optimizes scheduling schemes to minimize the bioassay completion time. Experimental results confirm that, compared with the state-of-the-art approach, our method shortened the bioassay completion time by an average of 40.9%.

Wavelength-routed optical networks-on-chip (WR-ONoCs) attract ever-increasing attention for supporting high-speed communications with low power and latency. Among all WRONoC routers, optical ring routers attract much interest for their simple structures. However, current designs of ring routers have overlooked the customization problem: when adapting to applications that have specific communication requirements, current designs suffer high propagation loss caused by long worst-case signal paths and high splitter usage in power distribution networks (PDN). To address those problems, we propose a novel customization method to generate application-specific ring routers with multiple sub-rings, SRing. Instead of sequentially connecting all nodes in a large ring, we cluster the nodes and connect them with sub-ring waveguides to reduce the path length. Besides, we propose a mixed integer linear programming model for wavelength assignment to reduce the number of PDN splitters. We compare SRing to three state-of-the-art ring router design methods for six applications. Experimental results show that SRing can greatly reduce the length of the longest signal path, the worst-case insertion loss, and the number of splitters in the PDN, significantly improving the power efficiency.

Inkjet-printed electronics has attracted considerable attention for low-cost mass production. High-density inkjet-printed designs can benefit from printing and drying in batches to avoid defects due to undesired ink redistribution and ink merging. The state-of-the-art approach decomposes the design into small objects, assigns the objects to different layers to be printed in different iterations, and minimizes the number of layers to reduce the number of iterations. However, it overlooks the differences in the printing and drying time between different layers and thus cannot properly model the impact of different layer assignment solutions on the manufacturing cycle time. In this work, we propose a row-based printing model that simulates the inkjet-printing mechanism and an integral Gaussian drying model that evaluates the local evaporation rate to approximate the printing and drying process of inkjet-printed manufacturing. Based on these models, we propose a mixed-integer-linear programming (MILP) method called the manufacturing model to minimize the manufacturing cycle time and avoid defects by optimally assigning objects to different iterations to be printed and dried in batches. Experimental results confirm that, compared with the preliminary work, manufacturing using our optimized solutions required up to 42.7% less time.

After decades of development, flow-based microfluidic biochips have become a revolutionary platform for biochemical experiments. To meet the increasingly complex experimental demands, the length and density of channels in these chips grow significantly, which brings about higher defect probabilities. Till now, several methods have been proposed to improve the yield of these increasingly complex chips. However, the effectiveness of these methods cannot be properly evaluated, since there has been no method that systematically analyzes the reliability of a microfluidic design. In this paper, we propose the first mathematical models to quantify the reliability of a microfluidic design by calculating the probability of blockage and leakage defects happening to the design. Besides, we propose a graph-transformer-based method to speed up the calculation, so that designers can have a fast and accurate evaluation of the reliability of a microfluidic design at any scale.

In high-performance printed circuit boards (PCBs), adding serpentine delay lines is the most prevalent delay-matching technique to balance the delays of time-critical signals. Serpentine topology, however, can induce simultaneous accumulation of the crosstalk noise, resulting in erroneous logic gate triggering and speed-up effects. The state-of-the-art approach for crosstalk alleviation achieves waveform integrity by enlarging wire separation, resulting in an increased routing area. We introduce a method that adopts spiral delay lines for delay matching to mitigate the speed-up effect by spreading the crosstalk noise uniformly in time. Our method avoids possible routing congestion while achieving a high density of transmission lines. We implement our method by constructing a mixed-integer-linear programming (MILP) model for routing and a quadratic programming (QP) model for spiral synthesis. Experimental results demonstrate that our method requires, on average, 31% less routing area than the original design. In particular, compared to the state-of-the-art approach, our method can reduce the magnitude of the crosstalk noise by at least 69%.

Inkjet-printed electronics have attracted considerable attention for low-cost mass production. To avoid undesired device behavior due to accidental ink merging and redistribution, high-density designs can benefit from layering and drying in batches. The overall manufacturing cycle-time, however, now becomes dominated by the cumulative drying time of these individual layers. The state-of-the-art approach decomposes the whole design, arranges the modified objects in different layers, and minimizes the number of layers. Fewer layers imply a reduction in the number of printing iterations and thus a higher manufacturing efficiency. Nevertheless, printing objects with significantly different drying dynamics in the same layer leads to a reduction of manufacturing efficiency, since the longest drying object in a given layer dominates the time required for this layer to dry. Consequently, an accurate estimation of the individual layers' drying time is indispensable to minimize the manufacturing cycle-time. To this end, we propose the first Gaussian drying model to evaluate the local evaporation rate in the drying process. Specifically, we estimate the drying time depending on the number, area, and distribution of the objects in a given layer. Finally, we minimize the total drying time by assigning to-be-printed objects to different layers with mixed-integer-linear programming (MILP) methods. Experimental results demonstrate that our Gaussian drying model closely approximates the actual drying process. In particular, comparing the non-optimized fabrication to the optimized results demonstrates that our method is able to reduce the drying time by 39%.